Comment reward resource issuing method and comment reward resource display method

Abstract

The invention relates to a comment reward resource issuing method and a comment reward resource display method. The comment reward resource issuing method comprises the steps that multiple comments issued for target issuing content are acquired, the target issuing content is used for describing a target product, and the target issuing content carries a transaction triggering control for the target product; for each comment, determining the transaction influence weight of the comment on the target product based on the multidimensional record data of the comment; based on the total reward resource amount of the target product and the transaction influence weight of each comment, determining a target comment capable of allocating reward resources and a reward resource amount allocated for the target comment; and issuing reward resources corresponding to the reward resource quantity to the issuing object of the target comment. Through the method, the user can be stimulated to publish more high-quality comments, the user stickiness and the interaction frequency are improved, and the transaction is promoted to be achieved, so that the utilization rate of interaction configuration resources and transaction configuration resources is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for distributing comment reward resources, as well as a method, apparatus, computer equipment, storage medium, and computer program product for displaying comment reward resources. Background Technology

[0002] With the development of internet technology, more and more users are using the internet for social activities and product promotion. Taking short video e-commerce as an example, it is a new business model that combines short videos and e-commerce. It showcases specific products through short videos, and the content of the short videos can describe the products to attract more users to complete transactions.

[0003] In traditional technologies, short video publishers may not be able to accurately express the audience's preferences and usage experiences through short video content. Viewers of short videos promoting products are not very interested, which leads to the inability to guarantee product promotion and sales. This can easily result in idle processing resources configured for this scenario, resulting in low utilization of processing resources. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer device, storage medium, and computer program product for distributing comment reward resources, as well as a method, apparatus, computer device, storage medium, and computer program product for displaying comment reward resources, in order to address the above-mentioned technical problems and improve the utilization rate of processing resources.

[0005] Firstly, this application provides a method for distributing comment reward resources. The method includes:

[0006] Retrieve multiple comments published in response to target content, wherein the target content describes a target product and carries a transaction trigger control for the target product;

[0007] For each of the aforementioned comments, based on the multidimensional record data of the comments, the weight of the comment's impact on the transaction of the target product is determined;

[0008] Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined.

[0009] The reward resources corresponding to the amount of reward resources are distributed to the recipient of the target comment.

[0010] Secondly, this application also provides a device for distributing comment reward resources. The device includes:

[0011] The comment acquisition module is used to acquire multiple comments published in response to target content, wherein the target content describes the target product and carries a transaction trigger control for the target product;

[0012] The weight determination module is used to determine the transaction impact weight of each comment on the target product based on the multi-dimensional record data of the comment.

[0013] The reward resource allocation module is used to determine the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, based on the total amount of reward resources for the target product and the transaction impact weight of each comment.

[0014] The reward resource distribution module is used to distribute the reward resources corresponding to the amount of reward resources to the recipient of the target comment.

[0015] In some embodiments, the weight determination module is configured to, for each comment, obtain target record data for each target dimension from the multidimensional record data of the comment; determine weight data to be assigned to each target record data based on each target record data and the weight matching method corresponding to the target dimension to which the target record data belongs; and perform weight fusion based on the weight data of each target record data to obtain the transaction impact weight of the comment on the target product.

[0016] In some embodiments, the multidimensional record data includes the ranking position of the comment in the comment section, the relevance of the comment content to the target product, the number of interactions with the comment, and transaction change data after the comment was published;

[0017] The target dimension of the target record data includes at least one of the multidimensional record data.

[0018] In some embodiments, the reward resource allocation module is further configured to sort the comments according to their respective transaction impact weights to obtain a comment sequence; filter target comments from the comment sequence that can be allocated reward resources according to the comment filtering conditions; and allocate the total reward resources to each target comment according to the resource allocation method matched with the total reward resources of the target product, thereby obtaining the amount of reward resources allocated to each target comment.

[0019] In some embodiments, the reward resource allocation module is further configured to, when the resource allocation conditions include a quantity condition, select the N comments with the largest transaction impact weight from the comment sequence as target comments, where N is the largest positive integer that satisfies the quantity condition; and when the resource allocation conditions include a quality condition, select the comments in the comment sequence that satisfy the quality condition as target comments.

[0020] In some embodiments, the reward resource allocation module is further configured to, when the target comments are arranged in the comment sequence from largest to smallest according to the transaction impact weight, determine the reward allocation ratio for each target comment based on the order of the target comments in the comment sequence; the reward allocation ratio is inversely correlated with the order of the target comments in the comment sequence; and allocate the total reward resources to each target comment according to the reward allocation ratio to obtain the amount of reward resources allocated to each target comment.

[0021] In some embodiments, the multidimensional record data includes the sorting position of the comment in the comment area; the comment reward resource distribution device further includes a comment recommendation module, used to obtain the quality weight data of each comment; sort the comments according to the quality weight data using a max-heap data structure to obtain the quality ranking result of each comment; filter out recommended comments that meet the recommendation criteria from the comments according to the quality ranking result, and adjust the sorting position of the recommended comments in the comment area.

[0022] In some embodiments, the comment recommendation module is specifically used to determine the upward movement range of the recommended comment in the comment area based on the initial sorting position of the recommended comment in the comment area and the quality sorting result of the recommended comment; and adjust the sorting position of the recommended comment in the comment area according to the upward movement range.

[0023] In some embodiments, the comment recommendation module is further configured to, for each comment, obtain the comment content and the number of interactions with the comment; perform content quality analysis on the comment content based on a large language model to obtain a first weight data for the comment in the content quality dimension; determine a second weight data for the comment in the interaction quality dimension based on the number of comments replied to and liked in the interaction count; determine a third weight data for the comment in the originality quality dimension based on the similarity between the comment and previously posted comments; and obtain quality weight data for each comment based on at least one of the first weight data, the second weight data, and the third weight data.

[0024] In some embodiments, the reward resource distribution module is specifically used to obtain the reward resource allocation period for the target product, determine the amount of reward resources allocated to the target comment during the reward resource allocation period, and distribute the reward resources corresponding to the amount of reward resources to the person who posted the target comment according to the reward resource allocation period.

[0025] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0026] Retrieve multiple comments published in response to target content, wherein the target content describes a target product and carries a transaction trigger control for the target product;

[0027] For each of the aforementioned comments, based on the multidimensional record data of the comments, the weight of the comment's impact on the transaction of the target product is determined;

[0028] Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined.

[0029] The reward resources corresponding to the amount of reward resources are distributed to the recipient of the target comment.

[0030] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0031] Retrieve multiple comments published in response to target content, wherein the target content describes a target product and carries a transaction trigger control for the target product;

[0032] For each of the aforementioned comments, based on the multidimensional record data of the comments, the weight of the comment's impact on the transaction of the target product is determined;

[0033] Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined.

[0034] The reward resources corresponding to the amount of reward resources are distributed to the recipient of the target comment.

[0035] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0036] Retrieve multiple comments published in response to target content, wherein the target content describes a target product and carries a transaction trigger control for the target product;

[0037] For each of the aforementioned comments, based on the multidimensional record data of the comments, the weight of the comment's impact on the transaction of the target product is determined;

[0038] Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined.

[0039] The reward resources corresponding to the amount of reward resources are distributed to the recipient of the target comment.

[0040] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for distributing comment reward resources acquire multiple comments published on target content. Since the target content describes the target product and carries transaction trigger controls for the target product, high-quality comments on the target content allow consumers to more comprehensively perceive the value of the target product, increasing their willingness to transact. For each comment, based on multi-dimensional record data, the transaction impact weight of the comment on the target product is determined, enabling the assessment of the comment's role in promoting transaction completion. Based on the total reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined. Reward resources corresponding to the amount of reward resources are then distributed to the recipients of the target comments. By distributing reward resources based on the degree of impact of the comments on transactions, users can be incentivized to publish more high-quality comments, increasing user stickiness and interaction frequency, promoting transaction completion, and thereby improving the utilization rate of interaction and transaction configuration resources.

[0041] Sixthly, this application provides a method for displaying comment reward resources. The method includes:

[0042] The content display page shows the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product.

[0043] In response to a comment posting operation targeting the target published content, the target comment is displayed in the comment section of the target published content;

[0044] In response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed, and the amount of reward resources is positively correlated with the weight of the target comment's impact on the transaction of the target product.

[0045] Seventhly, this application provides a comment reward resource display device. The method includes:

[0046] The published content display module is used to display target published content with added comment reward icons on the published content display page; the target published content describes the target product and includes transaction triggering controls for the target product;

[0047] The comment display module is used to display the published target comment in the comment section of the target published content in response to a comment posting operation on the target published content;

[0048] The reward resource display module is used to respond to the comment reward viewing operation and display the amount of reward resources obtained from commenting on the target product. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product.

[0049] In some embodiments, the comment reward resource display device further includes a sorting position update module, which displays interactive controls for the target comment corresponding to the target comment; and updates the sorting position of the target comment in the comment area according to the cumulative trigger count trend of the interactive controls when the target comment meets the high-quality comment matching conditions of the target product.

[0050] In some embodiments, the comment reward resource display device further includes a prompt information display module, which, in response to a comment reward viewing operation, displays the influencing factor that has the greatest impact on the transaction's influence weight in the target comment when the reward resource amount is greater than zero; and displays the reason for not issuing reward resources in the target comment when the reward resource amount is equal to zero.

[0051] In some embodiments, the comment display module is further configured to, in response to a comment posting operation for the target published content, display the posted target comment below the existing comments if the posted target comment meets the content similarity condition with existing comments in the comment section of the target published content.

[0052] Eighthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0053] The content display page shows the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product.

[0054] In response to a comment posting operation targeting the target published content, the target comment is displayed in the comment section of the target published content;

[0055] In response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed, and the amount of reward resources is positively correlated with the weight of the target comment's impact on the transaction of the target product.

[0056] Ninthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0057] The content display page shows the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product.

[0058] In response to a comment posting operation targeting the target published content, the target comment is displayed in the comment section of the target published content;

[0059] In response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed, and the amount of reward resources is positively correlated with the weight of the target comment's impact on the transaction of the target product.

[0060] Tenthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0061] The content display page shows the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product.

[0062] In response to a comment posting operation targeting the target published content, the target comment is displayed in the comment section of the target published content;

[0063] In response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed, and the amount of reward resources is positively correlated with the weight of the target comment's impact on the transaction of the target product.

[0064] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for displaying comment reward resources, by displaying target published content with added comment reward icons on the published content display page, can attract users to participate in commenting on the target published content through the comment reward icons. The target published content describes the target product and includes transaction triggering controls for the target product, which can improve the convenience of triggering transactions for the target product and increase the probability of transaction completion. In response to the comment posting operation for the target published content, the published target comment is displayed in the comment area of ​​the target published content; in response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product. After a user posts a comment, corresponding reward resources are issued according to the transaction impact weight of the target comment on the target product, which can incentivize users to post more high-quality comments, increase user stickiness and interaction frequency, promote transaction completion, and thus improve the utilization rate of interaction configuration resources and transaction configuration resources. Attached Figure Description

[0065] Figure 1 A diagram illustrating the application environment of the comment reward resource distribution method and the comment reward resource display method in one embodiment;

[0066] Figure 2 This is a flowchart illustrating a method for distributing comment reward resources in one embodiment;

[0067] Figure 3 This is a schematic diagram of the process for allocating reward resources in one embodiment;

[0068] Figure 4 This is a schematic diagram illustrating the process of sorting comments according to their quality in one embodiment;

[0069] Figure 5 This is a flowchart illustrating the comment reward resource distribution method in another embodiment;

[0070] Figure 6 This is a flowchart illustrating a method for displaying comment reward resources in one embodiment;

[0071] Figure 7 This is a schematic diagram of a page containing target published content with a comment reward indicator added in one embodiment.

[0072] Figure 8 This is a schematic diagram of a page corresponding to the amount of reward resources displayed for the target comment in one embodiment;

[0073] Figure 9 This is a structural block diagram of a comment reward resource distribution device in one embodiment;

[0074] Figure 10 This is a structural block diagram of a comment reward resource display device in one embodiment;

[0075] Figure 11 This is an internal structural diagram of a computer device in one embodiment;

[0076] Figure 12 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0078] The comment reward resource distribution method and comment reward resource display method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another server.

[0079] Specifically, for terminal 102, the published content display page of terminal 102 displays target published content with added comment reward icons. This target published content describes the target product and includes transaction trigger controls for the target product. In response to a comment posting operation on the target published content, terminal 102 displays the posted target comment in the comment section of the target published content. In response to a comment reward viewing operation, terminal 102 displays the amount of reward resources obtained from commenting on the target product. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product. That is, after a user posts a target comment, corresponding reward resources are issued based on the transaction impact weight of the target comment on the target product. This incentivizes users to post more high-quality comments, increases user stickiness and interaction frequency, promotes transaction completion, and thus improves the utilization rate of interaction configuration resources and transaction configuration resources. In some embodiments, the issued reward resources are virtual resources allocated to the comment posting object. Specifically, reward resources can be red envelopes, coupons, product vouchers, digital collectibles, etc.

[0080] For server 104, server 104 can obtain multiple comments published on the target content from terminal 102. The target content describes the target product and carries a transaction trigger control for the target product. For each comment, based on the multi-dimensional record data of the comment, the transaction impact weight of the comment on the target product is determined. Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments on which reward resources can be allocated and the amount of reward resources allocated to the target comments are determined. The reward resources corresponding to the amount of reward resources are distributed to the object that published the target comments, and the amount of reward resources distributed is displayed in terminal 102 so that users can view the distribution status of reward resources through terminal 102.

[0081] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0082] In one embodiment, such as Figure 2 As shown, a method for distributing reward resources is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0083] Step 202: Obtain multiple comments published in response to the target content. The target content describes the target product and carries a transaction trigger control for the target product.

[0084] Targeted content refers to content posted on an interactive platform that describes the target product. Users on the platform can comment on the targeted content to express their opinions and views. Targeted content can be one or more of the following: images, text, or videos. For example, targeted content could be a short video describing the target product, or multiple images of the target product.

[0085] A transaction trigger control for a target product refers to a control used to trigger a transaction for that target product. This control may contain a link to the transaction for that product. The terminal responds to the user's trigger action on the transaction trigger control, redirecting the user to the transaction page for the target product. On this page, the user can directly confirm the transaction order information, submit the transaction request, and complete the transaction for the target product.

[0086] Comments posted in response to the target content can be from any user on the interactive platform who browses the target content and posts a comment in the target content's comment section. These users include the user who posted the target content, and can be social media users connected to the user, such as friends added by the user on the interactive platform, followers of the user, or other users on the interactive platform.

[0087] The specific content of a comment directed at a target post depends on the viewpoint and detailed information the commenter intends to express. Comment content can be at least one of the following formats: text, images, or links. Comments directed at a target post can be direct, such as clicking a comment trigger control associated with the target post to open a comment input box, where the comment is entered and posted. Comments can also be indirect, such as further comments on existing comments, like accessing historical comments in the target post's comment section to open a reply input box, where the comment is entered and posted.

[0088] The interactive platform provides interactive controls for comments posted by users on target content. These controls include at least one of a like control and a reply control. The terminal can respond to a user's action of viewing the comment section triggered by the user browsing the target content, displaying the comment section content of the target content on the current page. The terminal can also respond to a user's action of liking or replying to a historical comment, recording the interaction data for that historical comment.

[0089] Specifically, each comment posted on the target content has multi-dimensional recorded data to comprehensively characterize the comment's quality and its impact on achieving a transaction for the target product. The terminal can generate recorded data for comments in real time or at regular intervals based on changes in comments posted on the target content. In some embodiments, while generating multi-dimensional recorded data for comments posted on the target content, relevant data on the transaction completion status of the target product can also be recorded.

[0090] Step 204: For each comment, determine the weight of the comment's impact on the target product's transaction based on the multidimensional record data of the comment.

[0091] The multidimensional recorded data includes data that characterizes the impact of comments on achieving transactions related to the target product from multiple dimensions. Specifically, the multidimensional recorded data may include at least one of the following: the comment's ranking position in the comment section, the relevance of the comment's content to the target product, the number of interactions with the comment, and transaction changes after the comment was posted. Figure 3 As shown, multidimensional record data can include comment sorting, evaluation content, evaluation time, number of likes, etc.

[0092] The transaction impact weight is used to characterize the degree to which published comments promote the transaction of the target product. The greater the impact on promoting the transaction of the target product, the larger the transaction impact weight value and the more reward resources are allocated. Conversely, the smaller the impact on promoting the transaction of the target product, the smaller the transaction impact weight value and the less reward resources are allocated.

[0093] Specifically, the sorting position information in the comment section indicates the ranking position of a specific comment within comments of the same level. Comments directly addressing the target content are considered to be of the same level, as are replies to the same historical comment. Comments of the same level can be sorted according to their quality or their impact on achieving a transaction for the target product; a higher ranking indicates better comment quality or a greater impact on achieving a transaction for the target product. In this embodiment, the sorting of comments in the comment section can be dynamically adjusted in real time based on the comment quality assessment results, and the sorting can be periodically and globally adjusted after each assessment of the weight of the impact on the target product's transaction.

[0094] The relevance information between the comment content and the target product is used to characterize the strength of the association between the comment content and the target product. For example, if the comment content describes the target product from certain perspectives, such as the user experience or advantages of the product, it indicates a strong relevance between the comment content and the target product. Because comment content is highly subjective, comments posted in the comment section of a target post may not be related to the target product. If the comment content is unrelated to the target product, it indicates a weak relevance. Therefore, the relevance information between the comment content and the target product can, to some extent, reflect the impact of the comment on the transaction of the target product. In a specific embodiment, keywords in the comment content can be extracted, and a similarity analysis can be performed between the extracted keywords and the feature words describing the target product in the target post content. Based on the similarity analysis results, the relevance information between the comment content and the target product can be generated.

[0095] The number of interactions with comments includes at least one of the following: the number of replies to comments and the number of likes. More replies indicate greater interest in the comment from the viewer, while more likes indicate greater agreement with the comment from the viewer. Therefore, the number of interactions with comments can, to some extent, reflect the comment's impact on the transaction of the target product.

[0096] The transaction data following a comment's publication includes increases in views of the target content, transaction value, and number of transactions. In practice, high-quality comments can facilitate transactions of the target product. The more significant the positive impact of a comment on a transaction, the more pronounced the increase will be in at least one of the following: a significant increase in views of the target content, a significant increase in transaction value, or a significant increase in the number of transactions. Conversely, if a comment does not contribute to a transaction, the increase in views of the target content, transaction value, and the number of transactions will remain relatively constant or increase only slightly.

[0097] In some specific embodiments, after obtaining multiple comments on the target content, the server can obtain multi-dimensional record data for each comment and analyze its transaction impact based on this data to obtain the comment's transaction impact weight on the target product. Specifically, the server can analyze each item in the multi-dimensional record data individually or combine multiple data points for analysis. The results of each analysis can be normalized to ensure they have the same evaluation magnitude. Finally, the normalized analysis results are merged to obtain the transaction impact weight of each comment on the target product.

[0098] Step 206: Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, determine the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments.

[0099] The total reward resources for the target product are the total resources allocated to those who post comments, and may be a portion of the target product's transaction volume. Specifically, the total reward resources for the target product can be a pre-set fixed value, or it can be allocated according to a set ratio so that the total reward resources increase as the target product's transaction volume increases.

[0100] The target comments for which reward resources can be allocated are at least a portion of the comments that target the published content. For example, the target comments for which reward resources can be allocated could be all comments published on the target published content, meaning that all those who post comments on the target published content can receive reward resources. As another example, the target comments for which reward resources can be allocated could be a subset of the comments published on the target published content; specifically, comments that meet certain conditions regarding transaction impact weight or other parameters must be eligible to receive reward resources.

[0101] Specifically, the reward resources allocated to target comments differ based on their varying transaction impact weights. The server can determine the amount of reward resources allocated to a target comment based on its configured resource allocation method and the individual transaction impact weight of each comment. The resource allocation method can be configured by the terminal responding to a configuration operation triggered by the configuration party on the resource allocation method configuration page, obtaining resource allocation parameters, and then uploading these parameters to the server. Specifically, the configuration party for the resource allocation method can be the same as the provider of the reward resources. For example, if the reward resources are provided by the provider of the target product, the server can receive the resource allocation method configured by the target product provider. Another example is when the target product provider awards a content publishing reward to the publisher of the target content, and the comment reward resources are provided by the publisher of the target content; in this case, the server can receive the resource allocation method configured by the publisher of the target content.

[0102] In some specific embodiments, such as Figure 3 As shown, in response to a resource allocation event, the server selects target comments from the comments that can be allocated reward resources according to the transaction impact weight of each comment. The server obtains resource allocation parameters from the terminal, determines the resource allocation method represented by the resource allocation parameters, and then calculates the amount of reward resources allocated to each target comment based on the total amount of reward resources for the target product and the transaction impact weight of each target comment. Finally, the reward resources are distributed to the recipient of the target comment.

[0103] Step 208: Distribute the reward resources corresponding to the amount of reward resources to the recipient of the target comment.

[0104] The target commenter can be identified by a unique identity information such as the commenter's account or ID. The server can then use this identity information to distribute reward resources corresponding to the amount of reward resources to the target commenter.

[0105] The amount of reward resources represents the quantity of reward resources distributed. Reward resources distributed to the recipient are virtual resources that can be directly used by the recipient. Specific reward resources can include virtual resources such as red envelopes, coupons, product vouchers, and digital collectibles.

[0106] In practical applications, the server can respond to a reward resource distribution event and distribute reward resources corresponding to the amount of reward resources posted by the target commenter. In some embodiments, the reward resource distribution event can be triggered periodically, such as on a daily, weekly, or monthly basis, with the server automatically triggering the event according to the distribution cycle. In other embodiments, the reward resource distribution event can be triggered based on a set event, such as when the transaction volume of the target product reaches a set value, or when the target product is sold out or removed from the platform. The specific triggering conditions for the reward resource distribution event can be configured according to the actual scenario.

[0107] The aforementioned method for distributing comment reward resources, by acquiring multiple comments published on the target content, leverages the fact that the target content describes the target product and contains transaction trigger controls for that product. High-quality comments on the target content allow consumers to more comprehensively perceive the value of the target product, increasing their willingness to transact. For each comment, based on multi-dimensional record data, the transaction impact weight of the comment on the target product is determined, thus identifying its role in facilitating transactions. Based on the total reward resources for the target product and the transaction impact weight of each comment, the target comments eligible for reward resources and the amount of reward resources allocated to them are determined. Reward resources corresponding to this amount are then distributed to the recipients of the target comments. By distributing rewards based on the comment's impact on transactions, users are incentivized to publish more high-quality comments, increasing user engagement and interaction frequency, promoting transactions, and ultimately improving the utilization rate of interaction and transaction configuration resources.

[0108] In some embodiments, for each comment, based on the multidimensional record data of the comment, the weight of the comment's impact on the transaction of the target product is determined, including:

[0109] For each comment, target record data for each target dimension is obtained from the multi-dimensional record data of the comment; based on each target record data and the weight matching method corresponding to the target dimension to which the target record data belongs, weight data is determined for each target record data; weight fusion is performed based on the weight data of each target record data to obtain the weight of the comment's impact on the transaction of the target product.

[0110] The target dimension is used to evaluate whether it will affect the transaction of the target product. The target dimension can be one or more dimensions represented by multidimensional record data. Specifically, the multidimensional record data can include the ranking position of comments in the comment section, the relevance of the comment content to the target product, the number of interactions with the comments, and transaction change data after the comments are published. The target record data for the target dimension includes at least one of the multidimensional record data.

[0111] For each target dimension, a specific weight matching method is pre-configured. Based on the target record data of the target dimension, the server can determine the weight data assigned to each target record data according to the weight matching method. For different target dimensions, since the specific target record data involved is different, the configured weight matching method can also be different. For example, taking the target dimension as the number of likes for comments, there are a total of 50 comments on the target content, with a total of 10,000 likes. The first 5 comments have 4,000, 2,500, 2,000, 1,000, and 500 likes respectively. The weight matching method is to assign weight data according to the ratio of the number of likes for each comment to the total number of likes for all comments. Therefore, the weight data for the first 5 comments are 0.4 / 0.24 / 0.2 / 0.1 / 0.05 respectively. As another example, taking the target dimension as the relevance between the comment content and the target product, the server obtains a relevance value with a range of (0.1) through relevance analysis. This relevance value can then be directly used as the weight data for this target dimension.

[0112] Weight fusion refers to the data processing procedure of combining weighted data from different target dimensions into a single transaction impact weight. Weight fusion can be achieved by averaging or summing the weighted data from different target dimensions, or by weighting the weighted data according to preset weighting parameters. Furthermore, the server can first normalize the weighted data from different target dimensions, and then perform weight fusion on the normalized weighted data to obtain the transaction impact weight of the review on the target product.

[0113] In this embodiment, target record data for the target dimension is obtained from multi-dimensional record data to determine the corresponding weight data, thereby enabling the screening of the target dimension and meeting the personalized evaluation needs of the target dimension. By integrating the weights of each target dimension, the transaction impact weight is obtained, which allows for the overall analysis of the degree of impact of a single comment on the transaction of the target product, improving the comprehensiveness and reliability of the transaction impact weight evaluation.

[0114] In some embodiments, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined based on the total amount of reward resources for the target product and the transaction impact weight of each comment, including:

[0115] The comments are sorted according to their respective transaction impact weights to obtain a comment sequence. Target comments for which reward resources can be allocated are selected from the comment sequence according to the comment filtering criteria. The total reward resources are allocated to each target comment according to the resource allocation method matching the total reward resources of the target product, resulting in the amount of reward resources allocated to each target comment.

[0116] The sorting of comments can be done by ranking each comment according to its respective transaction impact weight. Specifically, the sorting can be done by arranging the comments from largest to smallest transaction impact weight, with the comment with the largest transaction impact weight at the beginning of the comment sequence. Alternatively, in other embodiments, the sorting can be done by arranging the comments from smallest to largest transaction impact weight, with the comment with the largest transaction impact weight at the end of the comment sequence.

[0117] Comment filtering criteria are used to select target comments from a list of comments. Specifically, comment filtering criteria include at least one of quantity and quality criteria. Based on these criteria, high-quality target comments can be selected from the comment sequence, achieving effective filtering of high-quality comments.

[0118] In some embodiments, target comments for which reward resources can be allocated are selected from the comment sequence according to comment filtering criteria, including the following cases:

[0119] In the first scenario, where the resource allocation conditions include quantity conditions, the N comments with the highest transaction impact weight are selected from the comment sequence as target comments, where N is the largest positive integer that satisfies the quantity condition.

[0120] Scenario 2: If the resource allocation conditions include quality conditions, then comments in the comment sequence that meet the quality conditions will be selected as target comments.

[0121] Scenario 3: When resource allocation conditions include both quantity and quality conditions, first select N candidate comments with the highest transaction impact weight from the comment sequence, and then use the candidate comments that meet the quality condition as the target comments. Alternatively, use comments that meet the quality condition from the comment sequence as candidate comments, and if the number of candidate comments is greater than the largest positive integer N that meets the quantity condition, select the N candidate comments with the highest transaction impact weight from the candidate comments.

[0122] Furthermore, regarding the total amount of reward resources, the server can allocate the total reward resources to each target comment according to a pre-configured resource allocation method, thus obtaining the amount of reward resources allocated to each target comment. The resource allocation method can be based on the proportion of the transaction influence weight to the sum of the transaction influence weights of all target comments, determining the amount of reward resources allocated to each target comment. Alternatively, it can be based on the order of the transaction influence weights. In other embodiments, the resource allocation method can also be set according to the actual scenario, for example, determining the amount of reward resources allocated to each target comment based on the change in transaction influence weights within a period.

[0123] In this embodiment, the server sorts the comments according to their respective transaction impact weights to obtain a comment sequence. This facilitates the quick selection of target comments from the comment sequence that can be allocated reward resources based on the comment filtering criteria. During the resource allocation process, the server allocates the total reward resources to each target comment according to the resource allocation method matched with the total reward resources of the target product. This results in a reward resource amount allocated to each target comment, which can meet the resource allocation needs in different scenarios, expand the available scenarios for reward resource allocation, and thereby improve the utilization rate of interaction configuration resources and transaction configuration resources.

[0124] In some embodiments, the comment sequence is arranged sequentially according to the value of the transaction impact weight, which may include ascending or descending order. Specifically, the total reward resources are allocated to each target comment according to the resource allocation method matched with the total reward resources of the target product. The amount of reward resources allocated to each target comment includes:

[0125] Given that the target comments are arranged in descending order of their transaction impact weight in the comment sequence, the reward allocation ratio for each target comment is determined based on its order in the comment sequence; the reward allocation ratio is inversely correlated with the order in which the target comments are arranged in the comment sequence.

[0126] Correspondingly, when the target comments are arranged in the comment sequence according to their transaction impact weight from smallest to largest, the reward allocation ratio for each target comment is determined based on its order in the comment sequence; the reward allocation ratio is positively correlated with the order of the target comments in the comment sequence.

[0127] The reward allocation ratio for each target comment is determined based on its order within the comment sequence. Specifically, this can be achieved by directly determining the reward allocation ratio for each target comment at equal intervals according to its order of appearance. In other words, only the order of appearance is considered, without considering the specific value of the transaction influence weight. If the transaction influence weight changes but the order of appearance remains the same, the reward allocation ratio remains unchanged. Alternatively, both the order of appearance and the specific value of the transaction influence weight can be considered simultaneously to determine the reward allocation ratio for each target comment. If the transaction influence weight changes but the order of appearance remains the same, the reward allocation ratio will also change accordingly.

[0128] Furthermore, the server allocates the total reward resources to each target comment according to the reward distribution ratio, thus obtaining the amount of reward resources allocated to each target comment separately.

[0129] In this embodiment, the server can calculate the amount of reward resources allocated to each target comment according to the reward distribution ratio, thus achieving a fair distribution of reward resources. Specifically, when the total amount of reward resources changes with the transaction amount of the target product, the number of target comments participating in the allocation can vary, thereby attracting more commenters to post more high-quality comments, improving the interaction effect of the target content, and increasing the stickiness and trust between the target content publishers and commenters.

[0130] In some embodiments, the multidimensional record data includes the ranking position of comments in the comment section; the reward resource distribution method further includes: obtaining the quality weight data of each comment; sorting each comment according to the quality weight data using a max-heap data structure to obtain the quality ranking result of each comment; filtering out recommended comments that meet the recommendation criteria from the comments according to the quality ranking result, and updating the ranking position of the recommended comments in the comment section.

[0131] Quality weight data is used to evaluate the quality of published comments. Specifically, quality weight data can be evaluated using dimensions such as the quality score of the comment content by a large language model, the number of likes, and the number of replies. In other embodiments, the quality weight data can also be determined based on dimensions such as whether it is the first comment and the number of similar comments.

[0132] When sorting quality-weighted data, a max-heap structure can be used. A max-heap, also called a root heap, is a binary heap where the key of the root node (also called the top of the heap) is the maximum value among all nodes in the heap. In a max-heap, for any node A, if A is the parent node of B, then the value of A must be greater than or equal to the value of B. This property ensures that the root node of a max-heap always contains the maximum value in the heap. A max-heap can be a special type of complete binary tree and can be stored using an array to improve space utilization. Creating a max-heap starts from an unordered complete binary tree and continuously adjusts the node positions to satisfy the max-heap property. Specifically, starting from the last non-leaf node, the max-heap is constructed upwards to the root node. During each adjustment, the current node is compared with its children; if the current node's value is less than the value of its children, they are swapped, and the adjustment continues downwards until the entire binary tree satisfies the max-heap property. When inserting a new element into the max-heap, the new element is first added to the end of the array, and then the heap is adjusted according to the max-heap property. The specific method involves comparing the new element with its parent node. If the value of the new element is greater than the value of its parent node, they are swapped. This process continues upwards until a suitable position is found or the root node is reached. In this embodiment, using a max-heap data structure to sort comments according to quality weight data effectively improves the processing efficiency of sorting quality weight data.

[0133] Recommendation criteria refer to the conditions that comments must meet to be prioritized for viewing. These criteria might include meeting certain quality weight values ​​among all comments related to the target content. For example, the quality weight value might rank within the top M comments of the target content, or it might meet a minimum weight threshold. For content published on an interactive platform, the calculation method and recommendation criteria for comment quality weights can be the same. In some scenarios, a default calculation method and recommendation criteria can be set on the interactive platform. The target content publisher can adjust these criteria for personalized configuration. The terminal or server can then automatically update and cache the execution logic for the quality weight calculation method and recommendation criteria according to the configured parameters. When it's necessary to calculate updated quality weights or filter recommended comments, the updated calculation method or recommendation criteria can be directly retrieved from the cached data.

[0134] In this embodiment, the server can accurately measure the quality weight of each comment by obtaining its own quality weight data. The server sorts the comments according to the quality weight data using a max-heap data structure, which can quickly obtain the quality ranking results of each comment. Finally, the server filters out the recommended comments that meet the recommendation criteria from the comments according to the quality ranking results and updates the ranking position of the recommended comments in the comment area, so as to achieve priority display of recommended comments in the comment area.

[0135] In one embodiment, updating the sorting position of recommended comments in the comment section includes: determining the upward shift of recommended comments in the comment section based on the initial sorting position of recommended comments in the comment section and the quality sorting result of recommended comments; and adjusting the sorting position of recommended comments in the comment section according to the upward shift.

[0136] The initial ranking position of a recommended comment in the comment section refers to its position before any reordering. The specific calculation method for the upward movement can be set according to the actual scenario. Both the initial ranking position and the quality ranking of the recommended comment will affect the upward movement. The calculation principle is that the later the initial ranking position of the recommended comment and the earlier its quality ranking, the greater the upward movement.

[0137] In this embodiment, the server combines the initial ranking position of recommended comments in the comment section with the quality ranking results to calculate the upward movement of recommended comments within the comment section. This effectively improves the recommendation of high-quality comments and increases their exposure. In shopping scenarios, this allows for the recommendation of high-quality comments to users, enhancing their shopping experience. Simultaneously, it enables more accurate allocation of reward resources to high-quality comments, thereby incentivizing the authors to publish more high-quality comments and promoting the healthy development of the interactive platform's community.

[0138] In one embodiment, the reward resource distribution method further includes: for each comment, obtaining the comment content and the number of interactions with the comment; performing content quality analysis on the comment content based on a large language model to obtain the first weight data of the comment in the content quality dimension; determining the second weight data of the comment in the interaction quality dimension based on the number of comments replied to and the number of likes for the number of interactions; and determining the third weight data of the comment in the originality quality dimension based on the similarity between the comment and previously published comments.

[0139] Furthermore, the quality weight data for each comment is obtained, including obtaining the quality weight data for each comment based on at least one of the first weight data, the second weight data, and the third weight data.

[0140] The comment content refers to the content entered and posted by the commenter, which can be at least one of the following: text content, image content, web link, etc. In practice, the quality weighting data of a comment can be evaluated from multiple dimensions, specifically including at least one of the following dimensions: content quality, interaction quality, and originality quality.

[0141] Specifically, the content quality dimension can be obtained through content quality analysis of the comment content. This analysis can be performed using a large language model to determine the first weight of the comment in the content quality dimension. The interaction quality dimension can be analyzed based on the number of comments and likes. Specifically, the second weight of the comment in the interaction quality dimension can be determined based on the ratio of comments to likes per interaction. The originality quality dimension can be analyzed based on data such as whether it is the first comment and the number of similar comments. Specifically, the third weight of the comment in the originality quality dimension can be determined based on the similarity between the comment and previously posted comments.

[0142] like Figure 4 and Figure 5 As shown, when determining the quality weight data of comments and adjusting the ranking of comments, the server can combine at least one of the following weight data: first weight data of content quality dimension, second weight data of interaction quality dimension, and third weight data of originality quality dimension, to obtain a single quality weight data for evaluating a single comment. In a specific embodiment, the server can determine the evaluation dimension of the comment's quality weight data in response to an evaluation dimension configuration operation for the quality weight data.

[0143] In this embodiment, the server analyzes the quality weight data of comments using at least one of the following dimensions: content quality, interaction quality, and originality quality. This facilitates the sorting of multiple comments based on their quality dimensions, thereby quickly filtering out high-quality recommendations.

[0144] In one embodiment, the reward resource distribution method further includes: obtaining the reward resource allocation period for the target product, and determining the amount of reward resources allocated to the target comment during the reward resource allocation period. Further, distributing the reward resources corresponding to the amount of reward resources to the recipient of the target comment includes: distributing the reward resources corresponding to the amount of reward resources to the recipient of the target comment according to the reward resource allocation period.

[0145] The reward resources can be allocated periodically, with the allocation period representing the time interval for allocating reward resources. This allocation period can be a fixed time interval or a non-fixed time period, such as a day, a week, or a month. The server automatically triggers reward resource distribution events according to the allocation period, distributing the corresponding amount of reward resources to the recipients of the target comments. Alternatively, the allocation period can be triggered by a set event, such as the time period corresponding to the target product's transaction volume reaching a set value, or the time period from the target product's listing to it selling out or being removed from the shelves.

[0146] After obtaining the reward resource allocation period for the target product, the server determines the total amount of reward resources and the amount of reward resources allocated to the target comment within that period. Then, according to the reward resource allocation period, it distributes the corresponding reward resources to the commenter, achieving periodic reward resource distribution. When a comment exists across multiple reward resource allocation periods, the server can determine the comment's transaction impact weight on the target product within each period based on the comment's incremental data. This determines the amount of reward resources allocated to the target comment within each period, ensuring that the commenter receives appropriate reward resources in every reward resource allocation period.

[0147] In this embodiment, by distributing the allocated reward resources to the recipients of the target comments according to the reward resource allocation cycle, the reward resources can be periodically distributed to the recipients of the target comments, which can effectively incentivize them to publish more high-quality comments and improve the utilization rate of interactive resources.

[0148] In one embodiment, such as Figure 6 As shown, a method for displaying reward resources is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps:

[0149] Step 602: On the content display page, display the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product.

[0150] The published content display page is used to display published content, and can specifically be a display page within an interactive application. The published content display page shows one or more published content items. Multiple published content items constitute a content flow that can be displayed sequentially on the published content display page. In some embodiments, to facilitate full-screen display of published content, different published content items can be displayed by triggering a vertical swipe on the published content display page. For example, if the published content display page already displays the first published content item, a second published content item can be displayed in response to a swipe operation triggered on the published content display page.

[0151] Targeted content refers to content that includes a comment reward indicator and a transaction trigger control for the target product. For example... Figure 7 As shown, 702 represents the target published content, 704 is the comment reward indicator, and 706 is the transaction trigger control. Unlike ordinary published content where users can post comments, users can distinguish whether a published content is a target published content where posting a comment reward will earn reward resources by whether it includes a comment reward indicator. The display method of the comment reward indicator in the target published content can be configured based on different application scenarios. For example, the comment reward indicator can be overlaid on the target published content as a floating window, or it can be added to the target published content as an attached element. The comment reward indicator added to the target published content can have a certain degree of transparency so that it does not interfere with the viewer's viewing of the target published content. The size, transparency, and display position of the comment reward indicator in the target published content can all be adjusted according to actual needs.

[0152] The content posted in the target context describes the target product. Users on the interactive platform can comment on the target content to express their opinions and views. Target content can be one or more of the following: images, text, or videos. For example, target content could be a short video describing the target product, or multiple images of the target product.

[0153] A transaction trigger control for a target product refers to a control used to trigger a transaction for that target product. This control may contain a link to the transaction for that product. The terminal responds to the user's trigger action on the transaction trigger control, redirecting the user to the transaction page for the target product. On this page, the user can directly confirm the transaction order information, submit the transaction request, and complete the transaction for the target product.

[0154] Step 604: In response to the comment posting operation for the target published content, display the posted target comment in the comment section of the target published content.

[0155] The comment posting operation for the target published content includes at least one of the following: triggering a comment control within the target published content to post a comment, and triggering a reply control in the target published content's history to indirectly post a comment. On the published content display page, a comment control for the target published content is displayed. The terminal can respond to the viewer's triggering action on the comment control, activating the comment input box for the target published content. After entering comment content in the input box, a comment posting confirmation operation can be triggered, and the posted comment will then be displayed in the comment section of the target published content.

[0156] Furthermore, viewers of the target content can also trigger a comment posting action based on the target content's historical comments. For example, on the content display page, a comment section control for the target content is displayed. Viewers of the target content can trigger this control, and the terminal, in response, expands the comment section of the target content. Within the comment section, historical comments for the target content are displayed, and for each historical comment, a reply control is displayed. The terminal, in response to triggering the reply control, invokes the reply input box for that historical comment. After entering a comment in the input box, a comment posting confirmation action is triggered, and the posted comment is then displayed in the display area associated with that historical comment within the target content's comment section.

[0157] Step 606: In response to the comment reward viewing operation, display the amount of reward resources obtained from commenting on the target product. The amount of reward resources is positively correlated with the weight of the target comment's impact on the target product's transactions.

[0158] The comment reward viewing function is used to check the amount of reward resources earned for comments made on target content. Specifically, the comment reward viewing function can be triggered in various ways. On the interactive platform, there can be multiple entry points to trigger this function. For example, it can be displayed on the personal information viewing page, the content display page where the target content is located, or the reward resource distribution notification page, providing diverse triggering methods for the target commenters.

[0159] Furthermore, the server can respond to a review rewards viewing operation by displaying the amount of reward resources earned from reviewing the target product. In some embodiments, such as Figure 8As shown, when the same target commenter publishes multiple target comments within the same reward resource allocation period, the reward resource amount received by each target commenter can be displayed. The reward resource allocation period can be a fixed time interval or a non-fixed time period, such as a day, a week, or a month. The server automatically triggers reward resource distribution events according to the reward resource allocation period, distributing the reward resources corresponding to the amount of reward resources given to the target commenter. Alternatively, the reward resource allocation period can be triggered by a set event, such as the time period corresponding to the target product's transaction volume reaching a set value, or the time period from the target product's listing to selling out or being removed from the listing.

[0160] After obtaining the reward resource allocation period for the target product, the server determines the total amount of reward resources and the amount of reward resources allocated to the target comment within that period. Then, according to the reward resource allocation period, it distributes the corresponding reward resources to the commenter, achieving periodic reward resource distribution. In some embodiments, when a comment exists across multiple reward resource allocation periods, the server can determine the comment's transaction impact weight on the target product within each period based on incremental data. This determines the amount of reward resources allocated to the comment within each period, ensuring that the commenter receives appropriate reward resources in each reward resource allocation period. By distributing the allocated reward resources to the commenter according to the reward resource allocation period, reward resources can be periodically distributed, effectively incentivizing them to post more high-quality comments and improving the utilization rate of interactive resources.

[0161] The aforementioned method for displaying comment reward resources, by showing target content with a comment reward icon on the content display page, can attract users to participate in commenting on the target content. The target content describes the target product and includes transaction trigger controls for that product, improving the ease of triggering transactions and increasing the likelihood of successful transactions. In response to the comment posting action, the posted comment is displayed in the comment section of the target content; in response to the comment reward viewing action, the amount of reward resources earned from commenting on the target product is displayed. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product. After a user posts a comment, corresponding reward resources are distributed based on the transaction impact weight of the target comment on the target product. This incentivizes users to post more high-quality comments, increases user stickiness and interaction frequency, promotes transaction completion, and thus improves the utilization rate of interaction and transaction configuration resources.

[0162] In some embodiments, the reward resource display method further includes: displaying an interactive control for the target comment corresponding to the target comment; and updating the ranking position of the target comment in the comment section according to the cumulative trigger count trend of the interactive control, provided that the target comment meets the high-quality comment matching criteria of the target product.

[0163] Specifically, the criteria for high-quality comments can be used to determine whether the content of a target comment is of high quality. Interactive controls for the target comment include reply controls and like controls. The more replies a comment receives, the greater the interest of the viewers in that comment; the more likes a comment receives, the more viewers agree with that comment.

[0164] The trend of the cumulative trigger count of interactive controls can specifically be a change in the ratio of the cumulative trigger count of the interactive control to the cumulative trigger count of all historical comments, or the rate of change of the cumulative trigger count of the interactive control. If the target comment meets the matching criteria for a high-quality comment of the target product, and the ratio of the cumulative trigger count of the target comment's interactive controls to the cumulative trigger count of all historical comments increases, it indicates a higher probability that the target comment is a high-quality comment. Therefore, the target comment's ranking position in the comment section can be moved up. If the cumulative trigger count of the target comment's interactive controls is increasing, but the ratio of the target comment's cumulative trigger count to the cumulative trigger count of all historical comments is decreasing, it indicates a lower probability that the target comment is a high-quality comment. In this case, the target comment's ranking position in the comment section can remain unchanged, or the target comment's ranking position can be moved down when other high-quality comments need to be moved up.

[0165] In this embodiment, by displaying interactive controls corresponding to the target comment, other users can be provided with an entry point to express their opinions or attitudes towards the target comment. When the target comment meets the criteria for a high-quality comment for the target product, the ranking position of the target comment in the comment section is updated based on the cumulative trigger count of the interactive controls. This can move high-quality comments upwards, achieving a recommendation effect and improving the user's transaction experience, thereby increasing the transaction volume of the target product. Furthermore, by rewarding high-quality comments, the user who posted the target comment can be incentivized to post more high-quality comments, thus promoting the healthy development of the virtual community formed by the interactive application.

[0166] In some embodiments, the reward resource display method further includes: in response to a comment reward viewing operation, if the amount of reward resources is greater than zero, displaying the influencing factor that has the greatest impact on the transaction impact weight in the target comment, corresponding to the target comment.

[0167] In this context, a reward resource quantity greater than zero indicates that the published target comment has received the corresponding reward resources. By displaying the influencing factors that have the greatest impact on the transaction impact weight in the target comment, it is easier for the publisher of the target comment to understand the main reason for receiving reward resources for publishing the target comment, thereby better incentivizing the publisher to publish more high-quality comments.

[0168] In other embodiments, the reward resource display method further includes: in response to a comment reward viewing operation, if the reward resource amount is zero, displaying the reason why no reward resource was issued for the target comment.

[0169] In this context, a reward resource amount of zero indicates that the published target comment did not receive the corresponding reward resources. By displaying the reason why the reward resources were not distributed, the person who published the target comment can understand the main reason why they did not receive the reward resources for publishing the target comment. This allows them to summarize ways to improve and incentivize the person who published the comment to publish more high-quality comments.

[0170] In some embodiments, in response to a comment posting operation on target published content, displaying the posted target comment in the comment section of the target published content includes: in response to a comment posting operation on target published content, if the posted target comment and an existing comment in the comment section of the target published content meet the content similarity condition, displaying the posted target comment below the existing comment.

[0171] Specifically, if the target comment and existing comments in the comment section of the target content meet the content similarity condition, it indicates that the probability of the target comment being an original comment is relatively low. Therefore, when the target comment and existing comments in the comment section of the target content meet the content similarity condition, the target comment is displayed below the existing comments to achieve the effect of prioritizing the display of original comments, thereby increasing the exposure of original comments. This allows original comments to receive higher reward resources compared to similar comments published later, thus better incentivizing the original comment publisher to publish more high-quality original comments.

[0172] This application also provides an application scenario where users can earn comment rewards by posting comments on a shopping showcase within a short video. This application scenario utilizes the aforementioned comment reward resource distribution method. Specifically, the application of this comment reward resource distribution method in this application scenario is as follows:

[0173] In existing technologies, short video shopping showcases are primarily described by individual video publishers to attract more users. However, video publishers may not accurately express the audience's preferences and user experiences, while high-quality reviews can often make potential customers perceive the product's value, thereby generating a purchase intention. This application proposes a system for sharing shopping showcase revenue from highly-rated reviews in short video e-commerce. This system includes a review recommendation module, a review ranking module, a reward resource calculation module, and a reward resource distribution module. The system is written in Java or C++, uses KV and MySQL for database storage, utilizes the message processing system within the interactive platform for message passing, and is implemented using a microservice architecture. Modules collaborate via RPC calls, and an AI model is introduced to score reviews and assist in review ranking.

[0174] Specifically, after publishing a video, the video publisher can set the product's revenue sharing ratio, the number of comments eligible for sharing, and the minimum threshold for comments to participate in the revenue sharing. The comment recommendation module will activate its comment recommendation capability, gradually promoting comments with sales potential and genuine, effective, and high-quality feedback on the product. The server will progressively calculate the impact of each comment on the product's transaction volume, rank the comments based on their impact, and allocate different revenue sharing weights to different comments. Once a product transaction is completed, the reward resource calculation module will calculate the revenue sharing ratio that the current comment can receive based on the number of likes, ranking, and weight of the comment. Finally, the reward resource distribution module, which is integrated with the payment system, will distribute the transaction amount, thus driving product transactions.

[0175] The comment recommendation module is a core component of this system. Its main function is to analyze and rank user comments, recommending high-quality comments to potential shoppers. This module uses multiple weighting dimensions, such as the number of comment replies, the number of likes, the comment quality score based on the GPT model, the number of first-time comments, and the number of similar comments, to dynamically push higher-weighted comments in the current short video to the top for easier viewing by users.

[0176] Specifically, the comment recommendation module first collects and analyzes user comments, then sorts them using a max-heap data structure. The sorting is based on multiple weighted dimensions, including the number of replies, likes, and comment quality score. The comment quality score is an AI-based score using the GPT model. Furthermore, the module provides additional rewards for first-time comments and similar comments to incentivize users to post more high-quality comments. In this way, the module dynamically recommends higher-weighted comments to users.

[0177] In terms of module composition, the comment recommendation module mainly consists of a data collection section, a comment analysis section, and a comment recommendation section. The data collection section is responsible for collecting user comment data, including comment content, number of likes, and number of replies. The comment analysis section is responsible for processing the collected data, including calculating comment weights and AI scoring based on the GPT model. The comment recommendation section is responsible for recommending comments with higher weights to users based on the analysis results.

[0178] The review recommendation module uses in-depth analysis of user reviews to identify high-quality comments and recommend them to users, thereby improving the user shopping experience and increasing product sales. Rewarding high-quality reviews incentivizes users to post more excellent reviews, thus promoting the healthy development of the community.

[0179] The comment impact ranking module is responsible for statistically analyzing and calculating the impact of each comment on the current product's transaction volume. This ranking of comments is used to assign revenue sharing weights to different comments. Factors such as the time a comment appears on a short video and the number of likes it receives are considered to more accurately measure the impact of each comment on product transactions.

[0180] The comment effectiveness ranking module first collects and analyzes user comment data, including comment content, comment time, and number of likes. Then, by statistically analyzing factors such as the comment's appearance and duration in the top 10, short video views, and product showcase transaction volume, this module comprehensively calculates the impact of each comment on product transaction volume, thus ranking the comments based on their effectiveness. Furthermore, the module assigns different revenue sharing weights to different comments based on the ranking results, thereby rewarding high-performing comments.

[0181] In terms of module composition, the comment effectiveness ranking module mainly consists of a data collection section, a data analysis section, and a comment ranking section. The data collection section is responsible for collecting user comment data, including comment content, comment time, number of likes, etc. The data analysis section is responsible for processing the collected data, including calculating the appearance and duration of comments in the top 10, short video views, and product showcase transaction volume. The comment ranking section is responsible for ranking the comments based on the data analysis results, thereby determining the impact of each comment on product transaction volume.

[0182] Through in-depth analysis of user reviews, the review ranking module can identify and sort reviews that significantly impact product sales for easy user viewing. Ranking high-quality reviews allows users to clearly see which reviews have a major impact on sales, thereby improving the user shopping experience and increasing sales. Rewarding high-quality reviews incentivizes users to post more excellent reviews, thus promoting the healthy development of the community.

[0183] The reward resource calculation module is responsible for calculating the reward amount for a single comment based on the reward resource weights and revenue sharing ratios configured by the video publisher. This module calculates the value of each comment to determine its weight within each unit of transaction value, and then distributes the reward according to the revenue sharing ratios set by the video publisher.

[0184] The reward resource calculation module first obtains the reward resource weights and revenue sharing ratios configured by the video publisher, as well as the comment effectiveness ranking results obtained by the comment effectiveness ranking module. Next, the reward resource calculation module calculates the weight of each comment per unit of transaction value based on the formula for calculating the effectiveness value of a single comment: (weight of time configuration + weight of views) / transaction amount. Then, based on the revenue sharing ratio set by the video publisher, it calculates the revenue sharing ratio that the current comment can receive. Because the effectiveness of comments in the comment section exhibits a long tail shape, comments with less than the top 50 effectiveness receive no rewards.

[0185] In terms of module composition, the reward resource calculation module mainly consists of a data acquisition section, a comment value calculation section, and a reward resource calculation section. The data acquisition section is responsible for obtaining the reward resource weights and revenue sharing ratios configured by the video publisher, as well as the comment ranking results obtained from the comment value ranking module. The comment value calculation section is responsible for calculating the weight of each comment in each yuan of transaction amount according to a formula. The reward resource calculation section, based on the calculated weights and the revenue sharing ratios set by the video publisher, calculates the revenue sharing ratio that the current comment can receive.

[0186] The reward resource calculation module calculates the value of each individual comment, thus determining its weight in each unit of transaction value. Rewards are then distributed according to the revenue sharing ratio set by the video publisher. This incentivizes users to post more high-quality comments, thereby increasing product transaction value. Rewarding high-quality comments encourages users to post even more high-quality comments, leading to increased transaction value and promoting a healthy community. Furthermore, the reward resource calculation module helps video publishers allocate reward resources more fairly, thereby enhancing trust between publishers and users.

[0187] The reward resource distribution module is responsible for paying out reward resources for actual transactions. This module connects to the payment system of the interactive platform, and based on the rewardable amount for each comment calculated by the reward resource calculation module, it completes the reward distribution for the transaction amount, thereby promoting product transactions.

[0188] The reward resource distribution module first obtains the rewardable amount for each comment calculated by the reward resource calculation module. Then, this module connects to the payment system of the interactive platform to complete the reward distribution for the transaction amount, thereby achieving secure and fast payment delivery.

[0189] In terms of module composition, the reward resource distribution module mainly consists of a data acquisition section, a payment system access section, and a reward resource distribution section. The data acquisition section is responsible for obtaining the rewardable amount for each comment calculated by the reward resource calculation module. The payment system access section is responsible for connecting to the interactive platform's payment system to achieve secure and fast payment delivery. The reward resource distribution section is responsible for distributing rewards based on the acquired data.

[0190] The reward distribution module integrates with the interactive platform's payment system to achieve secure and rapid payment delivery, thereby incentivizing users to post more high-quality comments and increasing product transaction volume. Rewarding high-quality comments encourages users to post even more high-quality content, thus increasing transaction volume and promoting healthy community development. Furthermore, the reward distribution module helps video publishers allocate reward resources more fairly, thereby enhancing the trust relationship between video publishers and users.

[0191] This solution introduces a complete comment recommendation and reward distribution system designed to incentivize users to post high-quality comments, thereby attracting more potential customers to make purchases. First, the comment recommendation module dynamically elevates comments with higher weight in the current short video through multi-dimensional weight calculations, giving high-quality comments more exposure. Second, the comment impact ranking module ranks comments based on their contribution to the current product's transaction volume and assigns different revenue sharing weights accordingly. Then, the reward resource calculation module calculates the reward amount for each comment based on the reward resource weights and revenue sharing ratios configured by the video publisher. Finally, the reward resource distribution module distributes reward resources to the comment posters.

[0192] The above approach firstly increases potential customers' recognition of the product by incentivizing users to post high-quality reviews, thereby boosting sales. Secondly, it builds a natural video account e-commerce recommendation and review community, promoting its healthy development. Finally, by fairly distributing reward resources, it strengthens the trust relationship between video publishers and users, which is conducive to long-term stable user engagement and business cooperation.

[0193] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0194] Based on the same inventive concept, this application also provides a comment reward resource distribution device for implementing the comment reward resource distribution method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more of the comment reward resource distribution device embodiments provided below can be found in the limitations of the comment reward resource distribution method described above, and will not be repeated here.

[0195] In one embodiment, such as Figure 9 As shown, a comment reward resource distribution device is provided, including: a comment acquisition module 902, a weight determination module 904, a reward resource allocation module 906, and a reward resource distribution module 908, wherein:

[0196] The comment acquisition module 902 is used to acquire multiple comments published in response to target content, wherein the target content describes the target product and carries a transaction trigger control for the target product;

[0197] The weight determination module 904 is used to determine the transaction impact weight of each comment on the target product based on the multi-dimensional record data of the comment.

[0198] The reward resource allocation module 906 is used to determine the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, based on the total amount of reward resources for the target product and the transaction influence weight of each comment.

[0199] The reward resource distribution module 908 is used to distribute the reward resources corresponding to the amount of reward resources to the recipient of the target comment.

[0200] In some embodiments, the weight determination module 904 is configured to, for each comment, obtain target record data for each target dimension from the multidimensional record data of the comment; determine weight data to be assigned to each target record data based on each target record data and the weight matching method corresponding to the target dimension to which the target record data belongs; and perform weight fusion based on the weight data of each target record data to obtain the transaction impact weight of the comment on the target product.

[0201] In some embodiments, the multidimensional record data includes the ranking position of the comment in the comment section, the relevance of the comment content to the target product, the number of interactions with the comment, and transaction change data after the comment was published;

[0202] The target dimension of the target record data includes at least one of the multidimensional record data.

[0203] In some embodiments, the reward resource allocation module 906 is further configured to sort the comments according to their respective transaction impact weights to obtain a comment sequence; filter target comments from the comment sequence that can be allocated reward resources according to the comment filtering conditions; and allocate the total reward resources to each target comment according to the resource allocation method matched with the total reward resources of the target product, thereby obtaining the amount of reward resources allocated to each target comment.

[0204] In some embodiments, the reward resource allocation module 906 is further configured to, when the resource allocation conditions include a quantity condition, select the N comments with the largest transaction impact weight from the comment sequence as target comments, where N is the largest positive integer that satisfies the quantity condition; and when the resource allocation conditions include a quality condition, select the comments in the comment sequence that satisfy the quality condition as target comments.

[0205] In some embodiments, the reward resource allocation module 906 is further configured to, when the target comments are arranged in the comment sequence from largest to smallest according to the transaction influence weight, determine the reward allocation ratio for each target comment based on the order of the target comments in the comment sequence; the reward allocation ratio is inversely correlated with the order of the target comments in the comment sequence; and allocate the total reward resources to each target comment according to the reward allocation ratio to obtain the amount of reward resources allocated to each target comment.

[0206] In some embodiments, the multidimensional record data includes the sorting position of the comment in the comment area; the comment reward resource distribution device further includes: a comment recommendation module, used to obtain the quality weight data of each comment; sort each comment according to the quality weight data using a max-heap data structure to obtain the quality ranking result of each comment; filter out recommended comments that meet the recommendation conditions from the comments according to the quality ranking result, and adjust the sorting position of the recommended comments in the comment area.

[0207] In some embodiments, the comment recommendation module is specifically used to determine the upward movement range of the recommended comment in the comment area based on the initial sorting position of the recommended comment in the comment area and the quality sorting result of the recommended comment; and adjust the sorting position of the recommended comment in the comment area according to the upward movement range.

[0208] In some embodiments, the comment recommendation module is further configured to, for each comment, obtain the comment content and the number of interactions with the comment; perform content quality analysis on the comment content based on a large language model to obtain a first weight data for the comment in the content quality dimension; determine a second weight data for the comment in the interaction quality dimension based on the number of comments replied to and liked in the interaction count; determine a third weight data for the comment in the originality quality dimension based on the similarity between the comment and previously posted comments; and obtain quality weight data for each comment based on at least one of the first weight data, the second weight data, and the third weight data.

[0209] In some embodiments, the reward resource distribution module 908 is specifically used to obtain the reward resource allocation period for the target product, determine the amount of reward resources allocated to the target comment during the reward resource allocation period, and distribute the reward resources corresponding to the amount of reward resources to the person who posted the target comment according to the reward resource allocation period.

[0210] The aforementioned comment reward resource distribution device acquires multiple comments published on target content. Since the target content describes the target product and carries transaction trigger controls for the target product, high-quality comments on the target content allow consumers to more comprehensively perceive the value of the target product, increasing their willingness to transact. For each comment, based on the multi-dimensional record data of the comment, the transaction impact weight of the comment on the target product is determined, thus determining the comment's role in promoting transaction completion. Based on the total reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined. The reward resources corresponding to the amount of reward resources are then distributed to the recipients of the target comments. By distributing corresponding reward resources based on the degree of impact of the comments on transactions, users can be incentivized to publish more high-quality comments, increasing user stickiness and interaction frequency, promoting transaction completion, and thereby improving the utilization rate of interaction configuration resources and transaction configuration resources.

[0211] Each module in the aforementioned comment reward resource distribution device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0212] Based on the same inventive concept, this application also provides a comment reward resource display device for implementing the comment reward resource display method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more comment reward resource display device embodiments provided below can be found in the limitations of the comment reward resource display method described above, and will not be repeated here.

[0213] In one embodiment, such as Figure 10 As shown, a comment reward resource display device is provided, including: a published content display module 1002, a comment display module 1004, and a reward resource display module 1006, wherein:

[0214] The content display module 1002 is used to display target content with a comment reward icon on the content display page; the target content describes the target product and includes a transaction trigger control for the target product.

[0215] Comment display module 1004 is used to display the published target comment in the comment area of ​​the target published content in response to a comment posting operation for the target published content;

[0216] The reward resource display module 1006 is used to display the amount of reward resources obtained from commenting on the target product in response to the comment reward viewing operation. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product.

[0217] In some embodiments, the comment reward resource display device further includes a sorting position update module, which displays interactive controls for the target comment corresponding to the target comment; and updates the sorting position of the target comment in the comment area according to the cumulative trigger count trend of the interactive controls when the target comment meets the high-quality comment matching conditions of the target product.

[0218] In some embodiments, the comment reward resource display device further includes a prompt information display module, which, in response to a comment reward viewing operation, displays the influencing factor that has the greatest impact on the transaction's influence weight in the target comment when the reward resource amount is greater than zero; and displays the reason for not issuing reward resources in the target comment when the reward resource amount is equal to zero.

[0219] In some embodiments, the comment display module 1004 is further configured to, in response to a comment posting operation for the target published content, display the posted target comment below the existing comments if the posted target comment meets the content similarity condition with existing comments in the comment area of ​​the target published content.

[0220] The aforementioned comment reward resource display device, by displaying target published content with added comment reward icons on the published content display page, can attract users to participate in commenting on the target published content through the comment reward icons. The target published content describes the target product and includes transaction triggering controls for the target product, which can improve the convenience of triggering transactions for the target product and increase the probability of transaction completion. In response to the comment posting operation for the target published content, the published target comment is displayed in the comment area of ​​the target published content; in response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product. After a user posts a comment, corresponding reward resources are issued according to the transaction impact weight of the target comment on the target product, which can incentivize users to post more high-quality comments, increase user stickiness and interaction frequency, promote transaction completion, and thus improve the utilization rate of interaction configuration resources and transaction configuration resources.

[0221] The modules in the aforementioned comment reward resource display device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0222] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a method for distributing and displaying comment reward resources.

[0223] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for distributing and displaying comment reward resources. The display unit of the computer device is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0224] Those skilled in the art will understand that Figure 11 or Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0225] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0226] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0227] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0228] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0229] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0230] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0231] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for distributing comment reward resources, characterized in that, The method includes: Retrieve multiple comments published in response to target content, wherein the target content describes a target product and carries a transaction trigger control for the target product; For each of the aforementioned comments, based on the multidimensional record data of the comments, the weight of the comment's impact on the transaction of the target product is determined; Based on the total amount of reward resources for the target product and the transaction impact weight of each comment, the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, are determined. The reward resources corresponding to the amount of reward resources are distributed to the recipient of the target comment.

2. The method according to claim 1, characterized in that, For each of the comments, based on the multidimensional record data of the comment, the determination of the comment's impact weight on the target product's transaction includes: For each of the aforementioned comments, target record data for each target dimension is obtained from the multidimensional record data of the comments; Based on each target record data and the weight matching method corresponding to the target dimension to which the target record data belongs, weight data is determined for each target record data. The weights of each target record are fused to obtain the weight of the impact of the comment on the transaction of the target product.

3. The method according to claim 2, characterized in that, The multidimensional record data includes the ranking position of the comment in the comment section, the relevance of the comment content to the target product, the number of interactions with the comment, and transaction change data after the comment was published; The target dimension of the target record data includes at least one of the multidimensional record data.

4. The method according to claim 1, characterized in that, The process of determining the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, based on the total reward resources of the target product and the transaction impact weight of each comment, includes: The comments are sorted according to their respective transaction impact weights to obtain a comment sequence; Based on the comment filtering criteria, target comments for which reward resources can be allocated are selected from the comment sequence; According to the resource allocation method matched with the total reward resources of the target product, the total reward resources are allocated to each of the target comments to obtain the amount of reward resources allocated to each of the target comments.

5. The method according to claim 4, characterized in that, The step of selecting target comments from the comment sequence that can be allocated reward resources according to the comment filtering criteria includes at least one of the following: When the resource allocation conditions include a quantity condition, the N comments with the largest transaction impact weight are selected from the comment sequence as target comments, where N is the largest positive integer that satisfies the quantity condition; If the resource allocation conditions include quality conditions, then comments in the comment sequence that meet the quality conditions are selected as target comments.

6. The method according to claim 4, characterized in that, The method of allocating the total reward resources to each of the target comments according to the resource allocation method matched with the total reward resources of the target product, wherein the reward resources allocated to each of the target comments include: When the target comments are arranged in descending order of their transaction impact weight within the comment sequence, a reward allocation ratio is determined for each target comment based on their order within the comment sequence; the reward allocation ratio is inversely correlated with the order of the target comments within the comment sequence. The total amount of reward resources is allocated to each of the target comments according to the reward allocation ratio, so as to obtain the amount of reward resources allocated to each of the target comments.

7. The method according to claim 1, characterized in that, The multidimensional record data includes the sorting position of the comment in the comment section; the method further includes: Obtain the quality weight data for each of the aforementioned comments; By using a max-heap data structure, the comments are sorted according to the quality weight data to obtain the quality ranking results of the comments. Based on the quality ranking results, recommended comments that meet the recommendation criteria are selected from the comments, and the ranking position of the recommended comments in the comment section is adjusted.

8. The method according to claim 7, characterized in that, The adjustment of the sorting position of the recommended comments in the comment section includes: Based on the initial sorting position of the recommended comment in the comment section and the quality sorting result of the recommended comment, determine the upward movement of the recommended comment in the comment section; Adjust the sorting position of the recommended comments in the comment section according to the upward shift.

9. The method according to claim 7, characterized in that, The method further includes: For each of the aforementioned comments, obtain the comment content and the number of interactions with the comment; Based on a large language model, the content quality of the comment is analyzed to obtain the first weight data of the comment in the content quality dimension. Based on the number of comment replies and likes in the interaction count, the second weight data of the comment in the interaction quality dimension is determined; Based on the similarity between the comment and previously published comments, a third weighting data for the originality quality dimension of the comment is determined; The step of obtaining the quality weight data for each of the comments includes: Based on at least one of the first weight data, the second weight data, and the third weight data, the quality weight data for each comment is obtained.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Obtain the reward resource allocation period for the target product, and determine the amount of reward resources allocated to the target review during the reward resource allocation period; The step of distributing the reward resources corresponding to the amount of reward resources to the recipient of the target comment includes: According to the reward resource allocation cycle, the reward resources corresponding to the amount of reward resources are distributed to the recipients of the target comments.

11. A method for displaying comment reward resources, characterized in that, The method includes: The content display page shows the target content with a comment reward icon; the target content describes the target product and includes a transaction trigger control for the target product. In response to a comment posting operation targeting the target published content, the target comment is displayed in the comment section of the target published content; In response to the comment reward viewing operation, the amount of reward resources obtained from commenting on the target product is displayed, and the amount of reward resources is positively correlated with the weight of the target comment's impact on the transaction of the target product.

12. The method according to claim 11, characterized in that, The method further includes: Corresponding to the target comment, display interactive controls for the target comment; If the target comment meets the criteria for a high-quality comment for the target product, the ranking position of the target comment in the comment section is updated based on the cumulative trigger count of the interactive control.

13. The method according to claim 11, characterized in that, The method further includes at least one of the following: In response to the comment reward viewing operation, if the reward resource amount is greater than zero, the influence factor with the greatest impact on the transaction influence weight in the target comment is displayed for the target comment; In response to the comment reward viewing operation, if the reward resource amount is equal to zero, the reason why no reward resource was issued is displayed for the target comment.

14. The method according to claim 11, characterized in that, In response to a comment posting operation targeting the target content, the posted target comment is displayed in the comment section of the target content, including: In response to a comment posting operation targeting the target content, if the target comment and an existing comment in the comment section of the target content meet the content similarity condition, the target comment is displayed below the existing comment.

15. A device for distributing comment reward resources, characterized in that, The device includes: The comment acquisition module is used to acquire multiple comments published in response to target content, wherein the target content describes the target product and carries a transaction trigger control for the target product; The weight determination module is used to determine the transaction impact weight of each comment on the target product based on the multi-dimensional record data of the comment. The reward resource allocation module is used to determine the target comments for which reward resources can be allocated, and the amount of reward resources allocated to the target comments, based on the total amount of reward resources for the target product and the transaction impact weight of each comment. The reward resource distribution module is used to distribute the reward resources corresponding to the amount of reward resources to the recipient of the target comment.

16. A device for displaying comment reward resources, characterized in that, The device includes: The published content display module is used to display target published content with added comment reward icons on the published content display page; the target published content describes the target product and includes transaction triggering controls for the target product; The comment display module is used to display the published target comment in the comment section of the target published content in response to a comment posting operation on the target published content; The reward resource display module is used to respond to the comment reward viewing operation and display the amount of reward resources obtained from commenting on the target product. The amount of reward resources is positively correlated with the transaction impact weight of the target comment on the target product.

17. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 14.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 14.

19. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 14.

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