Information processing method

Abstract

The application discloses an information processing method, which comprises the following steps: generating evaluation summary information of a target store based on store information and a plurality of order evaluation information of the target store, wherein the evaluation summary information is used to represent a summarized evaluation expression of the target store; adding the evaluation summary information to page information of a preset page of the target store; and in response to an access request of the client to the preset page, sending the page information to the client to enable the client to display the preset page containing the evaluation summary information. According to the embodiment of the application, the store information and the order evaluation information of the target store are summarized to generate corresponding evaluation summary information, and the evaluation summary information is added to the page information of the preset page of the target store, so that the evaluation summary information is displayed when the preset page is displayed on the client, and the overall situation of the target store is quickly displayed through the evaluation summary information.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to an information processing method. Background Technology

[0002] Currently, online food ordering has become an indispensable part of people's daily lives. Before ordering, users usually browse the customer reviews of the store to determine whether the store's products meet their needs in terms of taste, hygiene, portion size, store features, etc.

[0003] Because shops typically have a large number of user reviews, it can take users a considerable amount of time to browse reviews that relate to aspects they care about. Therefore, related technologies use users' historical ordering data to identify tags corresponding to their ordering habits. These tags are then displayed on the shop's review page, guiding users to click on the tags to view user reviews related to their interests.

[0004] However, user behavior in the catering industry is highly variable, making accurate prediction difficult. Furthermore, the aforementioned technologies require first identifying user behavior tags, displaying these tags, and then showing corresponding user reviews after the user clicks on a tag—a cumbersome process. Additionally, a large number of user reviews are needed to reflect the overall performance of the restaurant. Summary of the Invention

[0005] This application proposes an information processing method to address the shortcomings of the prior art.

[0006] The first aspect of this application proposes an information processing method applied to a server, the method comprising:

[0007] Based on the store information and multiple order review information of the target store, a review summary information of the target store is generated. The review summary information is used to represent a summary evaluation of the target store.

[0008] Add the evaluation summary information to the page information of the preset page of the target store;

[0009] In response to a client's access request for the preset page, the page information is sent to the client so that the client displays the preset page containing the evaluation summary information.

[0010] In some embodiments of this application, generating the evaluation summary information of the target store based on the store information of the target store and multiple order evaluation information includes:

[0011] Based on the store information of the target store, determine the store category to which the target store belongs;

[0012] Retrieve the preset prompt text corresponding to the store category;

[0013] Based on the store information, the multiple order review information, and the preset prompt text, the review summary information of the target store is generated through a preset content generation model.

[0014] In some embodiments of this application, the preset prompt text includes prompt words corresponding to at least one evaluation dimension; the step of generating the evaluation summary information of the target store based on the store information, the multiple order evaluation information, and the preset prompt text through a preset content generation model includes:

[0015] The store information, the multiple order review information, and the prompt words for each review dimension in the preset prompt text are input into the preset content generation model, and the model outputs the review summary information corresponding to each review dimension.

[0016] In some embodiments of this application, adding the evaluation summary information to the page information of the preset page of the target store includes:

[0017] The evaluation summary information corresponding to each evaluation dimension is added to the page information of the preset page of the target store to obtain the page information corresponding to each evaluation dimension; the preset page includes the store homepage and / or the store evaluation page.

[0018] Store the mapping relationship between each evaluation dimension and the page information corresponding to each evaluation dimension.

[0019] In some embodiments of this application, sending the page information to the client in response to a client's access request for the preset page includes:

[0020] Obtain user information of the user to whom the client belongs from the client's access request for the preset page;

[0021] Based on the user information, the target evaluation dimension corresponding to the user is determined;

[0022] From the mapping relationship, obtain the page information corresponding to the target evaluation dimension;

[0023] The obtained page information is sent to the client.

[0024] In some embodiments of this application, the user information includes a user identifier, and each evaluation dimension includes multiple product attribute dimensions; determining the target evaluation dimension corresponding to the user based on the user information includes:

[0025] Based on the user identifier, obtain the user's historical behavior data;

[0026] Based on the historical behavior data, the product attribute with the highest correlation to the user is determined; the correlation is used to characterize the frequency of the user's operations on products with the product attribute in the historical behavior data;

[0027] From the multiple product attribute dimensions, determine the product attribute dimension to which the product attribute with the highest correlation belongs;

[0028] The determined product attribute dimensions will be used as the target evaluation dimensions for the user.

[0029] In some embodiments of this application, the user information includes user access time, and each evaluation dimension includes multiple time period dimensions; determining the target evaluation dimension corresponding to the user based on the user information includes:

[0030] From the multiple time period dimensions, determine the time period dimension to which the user's access time belongs;

[0031] The determined time period dimension will be used as the target evaluation dimension for the user.

[0032] In some embodiments of this application, the evaluation summary information includes evaluation summary text, summary basis information, and page interaction elements; the evaluation summary text is a text that summarizes and evaluates the target store, and the summary basis information is used to characterize the data on which the evaluation summary text is based;

[0033] After sending the page information to the client, the process further includes:

[0034] Receive user interaction data returned by the client, wherein the user interaction data is generated by the user triggering the page interaction elements in the preset page displayed by the client;

[0035] Based on the user interaction data corresponding to the evaluation summary information, the preset content generation model is optimized.

[0036] In some embodiments of this application, optimizing the preset content generation model based on the user interaction data corresponding to the evaluation summary information includes:

[0037] Based on multiple user interaction data corresponding to the same evaluation summary information, a weight value for the evaluation summary information is determined, and the weight value is used to characterize the degree of positive evaluation of the evaluation summary information by the user.

[0038] Based on multiple evaluation summary information and the weight values ​​of the multiple evaluation summary information, the model parameters of the preset content generation model are adjusted.

[0039] In some embodiments of this application, the evaluation summary information further includes multiple evaluation tag information, which is used to characterize statistical data of positive evaluations of different evaluation dimensions in the multiple order evaluation information;

[0040] The step of adding the evaluation summary information to the page information of the target store's preset page includes:

[0041] Add the multiple evaluation tag information to the page information of the preset page;

[0042] The page information is configured with carousel configuration information for the multiple evaluation tags, which is used to characterize the speed at which the client displays each evaluation tag in turn on the preset page.

[0043] In some embodiments of this application, the method further includes:

[0044] When the current summary period arrives, based on the target store's store information and multiple order review information corresponding to the current summary period, update the target store's review summary information, where the multiple order review information corresponding to the current summary period includes order review information newly added within the current summary period; or,

[0045] If the number of newly added order review information for the target store reaches a preset threshold, the review summary information of the target store is updated based on the store information of the target store and multiple order review information, including the newly added order review information.

[0046] A second aspect of this application provides an information processing method applied to a client, the method comprising:

[0047] Send an access request to the server for the preset page of the target store;

[0048] The server receives page information of the preset page returned by the server. The page information includes the evaluation summary information of the target store. The evaluation summary information is generated by the server based on the store information of the target store and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store.

[0049] Based on the page information, the preset page including the evaluation summary information is displayed.

[0050] In some embodiments of this application, the preset page includes the target store's homepage or store review page; the review summary information includes review summary text, summary basis information, and page interactive elements;

[0051] After displaying the preset page including the evaluation summary information, the system further includes:

[0052] The system detects that the page interaction element displayed on the preset page has been triggered, and obtains the user interaction data submitted by the user through the page interaction element.

[0053] The user interaction data is sent to the server.

[0054] In some embodiments of this application, the evaluation summary information further includes multiple evaluation tag information;

[0055] The step of displaying the preset page including the evaluation summary information based on the page information further includes:

[0056] Based on the carousel configuration information configured in the page information, the multiple evaluation tag information is carouseled on the preset page. The carousel configuration information is used to characterize the speed at which the client displays each of the evaluation tag information in turn on the preset page.

[0057] A third aspect of this application proposes an information processing system, which includes a server and a client.

[0058] The server is configured to generate a summary evaluation of the target store based on the store information and multiple order evaluations, wherein the summary evaluation is used to represent a generalized evaluation of the target store; add the summary evaluation to the page information of a preset page of the target store; and send the page information to the client in response to a client's access request for the preset page.

[0059] The client is configured to send an access request for the preset page to the server; receive page information of the preset page returned by the server; and display the preset page including the evaluation summary information based on the page information.

[0060] A fourth aspect of this application discloses an information processing apparatus applied to a server, the apparatus comprising:

[0061] The generation module is used to generate evaluation summary information for the target store based on the store information and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store.

[0062] An add module is used to add the evaluation summary information to the page information of the preset page of the target store;

[0063] The sending module is used to send the page information to the client in response to the client's access request for the preset page, so that the client displays the preset page containing the evaluation summary information.

[0064] A fifth aspect of this application provides an information processing apparatus for use on a client side, the apparatus comprising:

[0065] The sending module is used to send access requests for preset pages of the target store to the server.

[0066] The receiving module is used to receive page information of the preset page returned by the server. The page information includes the evaluation summary information of the target store. The evaluation summary information is generated by the server based on the store information of the target store and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store.

[0067] The display module is used to display the preset page, which includes the evaluation summary information, based on the page information.

[0068] The sixth aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method as described in the first or second aspect above.

[0069] The seventh aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method as described in the first or second aspect above.

[0070] The eighth aspect of this application provides a computer program product comprising a computer program that is executed by a processor to implement the method described in the first or second aspect.

[0071] Based on the information processing methods described in the first and second aspects above, this application has at least the following beneficial effects or advantages:

[0072] In this embodiment, by summarizing the store information and order review information of the target store, corresponding review summary information is generated and added to the page information of the target store's preset page. This allows the review summary information to be displayed directly on the preset page when the client displays the preset page, and the overall situation of the target store can be quickly displayed through the review summary information. Users can understand the overall situation of the store without spending a lot of time browsing the review list or receiving related operations to jump to the user review page of the relevant tag. This improves the efficiency of users understanding the store situation and enhances the reference value of the store reviews.

[0073] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0074] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0075] Figure 1 This is a schematic diagram illustrating an information processing system according to some exemplary embodiments;

[0076] Figure 2 This is a flowchart illustrating an information processing method applied to a server, according to some exemplary embodiments;

[0077] Figure 3 This is a flowchart illustrating an information processing method according to some exemplary embodiments;

[0078] Figures 4-8 This is a schematic diagram illustrating an evaluation summary information according to some exemplary embodiments;

[0079] Figure 9 This is a flowchart illustrating an information processing method applied to a client, according to some exemplary embodiments;

[0080] Figure 10 This is a schematic diagram illustrating the structure of an information processing apparatus according to some exemplary embodiments;

[0081] Figure 11 This is another structural schematic diagram of an information processing apparatus according to an exemplary embodiment;

[0082] Figure 12 This is a schematic diagram of the hardware structure of an electronic device according to an exemplary embodiment;

[0083] Figure 13 This is a schematic diagram illustrating the structure of a storage medium according to an exemplary embodiment. Detailed Implementation

[0084] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0085] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0086] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination," etc.

[0087] 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. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0088] Currently, in the food delivery ordering industry, after receiving a user's selection of a particular store, the client displays the corresponding store page for the user to place an order. In addition to displaying various products, the store page also displays the store's rating, previous customers' impressions of the store, and a list of user reviews, so that users can understand the overall situation of the store based on the above-mentioned multiple dimensions of information and decide whether to place an order. However, in general, the number of user reviews in the review list is quite large, making it difficult for users to quickly understand the overall situation of the store through the review list alone.

[0089] In related technologies, different keywords are set on the store page based on user preferences, such as "generous portions", "good taste", "affordable price", etc. By receiving the selection operation for a certain keyword, the user reviews corresponding to that keyword are displayed.

[0090] First, it requires receiving a user's selection of a specific keyword before displaying the corresponding user reviews, which is quite cumbersome. Second, because user preferences in the catering industry are difficult to pinpoint and predict, the keywords set may not be accurate. Furthermore, even if the keywords are set accurately, although the number of user reviews corresponding to a particular keyword is reduced compared to all user reviews of the store, it may still take users some time to understand the overall situation of the store.

[0091] Based on this, embodiments of this application provide an information processing method, apparatus, system, electronic device, and storage medium. The information processing method generates evaluation summary information for a target store on the server side based on store information and multiple order evaluation information. The evaluation summary information is used to characterize a summary evaluation of the target store. The evaluation summary information is added to the page information of a preset page of the target store. In response to a client's access request for the preset page, the page information is sent to the client so that the client displays the preset page containing the evaluation summary information.

[0092] In this embodiment, by summarizing the store information and order review information of the target store, corresponding review summary information is generated and added to the page information of the target store's preset page. This allows the review summary information to be displayed directly on the preset page when the client displays the preset page, and the overall situation of the target store can be quickly displayed through the review summary information. Users can understand the overall situation of the store without spending a lot of time browsing the review list or receiving related operations to jump to the user review page of the relevant tag. This improves the efficiency of users understanding the store situation and enhances the reference value of the store reviews.

[0093] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0094] Figure 1 This diagram illustrates the network architecture upon which the information processing method provided in this application is based. The network architecture includes a server and a client. The client communicates with the server via a network. The server can be a single server, a server cluster consisting of multiple servers, or a cloud server, etc. The client can be a laptop, desktop computer, mobile phone, tablet computer, etc. Alternatively, the client can refer to an application installed on a laptop, desktop computer, mobile phone, tablet computer, or similar device.

[0095] In practical applications, the server can pre-generate a summary of the target store's reviews based on the store information and multiple order reviews, and then add this summary to the page information of the target store's preset page.

[0096] The client receives the user's viewing action for a preset page, generates an access request for the preset page, and sends the access request to the server. After receiving the access request, the server sends the page information of the preset page, including evaluation summary information, to the client.

[0097] The client displays a preset page showing a summary of reviews for the target store as a reference for users to decide whether to place an order at the target store.

[0098] Figure 2 This is a flowchart illustrating an information processing method according to some embodiments of this application. This method is applied to the aforementioned server, such as... Figure 2 As shown, the method specifically includes the following steps 101-103.

[0099] Step 101: The server generates a summary of the target store's reviews based on the store information and multiple order reviews.

[0100] Evaluation summary information is used to represent a summary evaluation of the target store.

[0101] The store information can include store address, store category, and operating hours. Store category information can include dishes, beverages, and soups, etc. Order review information is generated upon receiving a user's order for the target store and their review of that order. Multiple order review information includes the review information for any single order from the target store.

[0102] In some embodiments of this application, at least one of store address information, store category information, and store operating time information can be combined with multiple order evaluation information to generate evaluation summary information of the target store.

[0103] For example, by combining store category information with multiple order reviews, a summary of reviews for the target store can be generated. This summary can be related to the store category information; for instance, if the store category is beverages, the summary could include phrases like "The store's beverages are delicious and sweet."

[0104] By combining store category information, store operating hours information, and multiple order reviews, a review summary of the target store is generated. This review summary can be related to the time period and store category. For example, if the store category is beverages and the operating hours are from 8:00 AM to 5:00 PM, the corresponding review summary could include: "The store's beverages sell best between 12:00 PM and 1:00 PM, and most users find the beverages to be delicious and sweet."

[0105] There are many other specific examples of generating a target store's review summary information by combining at least one of the following: store address information, store category information, and store operating time information, along with multiple order review information. These will not be listed one by one here.

[0106] Evaluation summary information may include evaluation summary text expressing the advantages of the target store, evaluation summary text expressing the disadvantages of the target store, the basis information for the evaluation summary information, and satisfaction information, etc.

[0107] In some embodiments of this application, the evaluation summary information may include multiple evaluation summary sub-information, each evaluation summary sub-information corresponding to a product of the target store, and each evaluation summary sub-information may include evaluation summary text expressing the advantages of the product, the basis information of the evaluation summary sub-information, and satisfaction information, etc.

[0108] Understandably, if the number of order reviews is greater than or equal to a preset threshold, a summary review of the target store can be generated based on multiple order reviews. If the number of order reviews is less than the preset threshold, the corresponding order reviews can be displayed on a preset page of the target store. On the one hand, because the number of order reviews is small, the generated summary review may not accurately reflect the overall situation of the target store. On the other hand, because the number of order reviews is small, directly displaying each order review allows users to quickly understand the overall situation of the target store; in this case, not generating a summary review saves server-side computing resources.

[0109] The preset quantity threshold can be flexibly set based on the actual situation, such as 50 items, 100 items, etc.

[0110] Step 102: The server adds the evaluation summary information to the page information of the target store's preset page.

[0111] Step 103: The server responds to the client's access request for the preset page by sending the page information to the client so that the client can display the preset page containing the evaluation summary information.

[0112] The preset page can be the target store's homepage or the target store's review page, etc.

[0113] Understandably, the server stores multiple page information corresponding to the client. When the client receives a display operation for a preset page, the client sends an access request to the server, and the server returns the corresponding page information. The client then displays the preset page containing the evaluation summary information based on this page information.

[0114] This application discloses an information processing method applied to the server side. By summarizing the store information and order review information of a target store, corresponding review summary information is generated and added to the page information of a preset page of the target store. This allows the review summary information to be displayed directly on the preset page when the client displays the preset page. The review summary information quickly displays the overall situation of the target store without requiring users to spend a lot of time browsing the review list or receiving further operations to jump to the user review page of the relevant tags. This improves the efficiency of users in understanding the store situation and enhances the reference value of store reviews.

[0115] In some embodiments of this application, generating evaluation summary information for a target store based on the store information of the target store and multiple order evaluation information includes: determining the store category to which the target store belongs based on the store information of the target store; obtaining a preset prompt text corresponding to the store category; and generating the evaluation summary information for the target store through a preset content generation model based on the store information, multiple order evaluation information and the preset prompt text.

[0116] The preset prompt text is used to guide the preset content generation model to generate the corresponding output. By providing specific prompts, it helps the preset content generation model to more accurately understand and answer questions. As mentioned above, store categories can include dishes, drinks, and soups, etc., so the preset prompt text corresponding to the store category can include relevant prompts about dishes, drinks, and soups. For example, the preset prompt text for dishes can include prompts related to spiciness, and the preset prompt text for drinks can include prompts related to sweetness, and so on.

[0117] Understandably, since store information and multiple order review information can be presented in various forms, such as audio, video, images, and text, there can also be various preset content generation models, such as large language models, multimodal models, etc. These models can all generate review summary information from store information, multiple order review information, and preset prompt text.

[0118] The input to a large language model can be text-based, such as text prompts or text sequences, while the input to a multimodal model can be data from multiple modalities (such as text, images, video, and audio). A multimodal model can also include various other models, such as a large language model, a computer vision model, and an audio recognition model.

[0119] When the store information, multiple order review information, and preset prompt text are all in text format, the preset content generation model can be a large language model or a multimodal model. The store information, multiple order review information, and preset prompt text can be input into the large language model or multimodal model to obtain the review summary information of the target store.

[0120] When store information is in video format and multiple order reviews are in audio format, the multimodal model can include a computer vision model, a large language model, and an audio recognition model. The store information, multiple order reviews, and preset prompt text are input into the multimodal model, which can recognize the content of the store information, the content of the preset prompt text, and the content of the multiple order reviews. The multimodal model then combines the recognized content to obtain a summary of the target store's reviews.

[0121] In summary, by combining store information and the presentation formats of multiple order review information, the corresponding preset content generation model can be selected to quickly obtain the review summary information of the target store.

[0122] As mentioned above, store information includes the store category to which the target store belongs. Therefore, the store category to which the target store belongs can be determined by obtaining the store information.

[0123] In an optional embodiment, the flowchart for generating review summary information based on store information, multiple order review information, and preset prompt text is as follows: Figure 3 As shown, a preset content generation model obtains store information and multiple order reviews for the target store. This information is stored in an order review database. A preset prompt text corresponding to the store category is retrieved. The preset content generation model then uses this prompt text, combined with the store information and order reviews, to generate an initial review summary. This initial review summary undergoes risk control verification, specifically checking for sensitive text. If the initial review summary passes the risk control verification, it is stored in an offline database. If it fails, it is discarded. Upon receiving a user's access request for a preset page, the online database synchronously retrieves the review summary information from the offline database and uses the preset content generation model to consume this information for display on the preset page.

[0124] As an example, preset prompt text can be used to prompt users to rate a target store based on the product's taste, texture, portion size, and price. The preset content generation model can then extract user reviews corresponding to these aspects from multiple order reviews and generate a summary of the evaluation information related to these factors. The final evaluation summary could include, for example, "The reviews repeatedly mention the crispy texture, rich flavor, generous portion size, and affordable price; it's a hidden gem store worth trying."

[0125] In this embodiment of the application, by using the preset prompt text corresponding to the store category, the preset content generation model is prompted to output evaluation summary information based on store information and multiple order evaluation information. This can improve the understanding ability of the preset content generation model and the accuracy of the output evaluation summary information. Furthermore, by outputting evaluation summary information through the preset content generation model, the efficiency and accuracy of the evaluation summary information can be improved.

[0126] In some embodiments of this application, the preset prompt text includes prompt words corresponding to at least one evaluation dimension; based on store information, multiple order evaluation information, and the preset prompt text, a preset content generation model generates evaluation summary information of the target store, including:

[0127] Input the store information, multiple order review information, and prompt words for each review dimension from the preset prompt text into the preset content generation model, and output the review summary information corresponding to each review dimension.

[0128] The evaluation dimensions can include product attribute dimensions and time period dimensions, among others. Taking the food attribute dimension within the product attribute dimension as an example, the food attribute dimension can include: food flavor, food texture, food portion size, and food price, etc. Each food attribute dimension can be further subdivided; for example, food flavor can include sweet and spicy, and food texture can include smooth, creamy, etc. The preset prompt text can include prompt words corresponding to one or more of the above evaluation dimensions.

[0129] For any given evaluation dimension, the preset content generation model obtains user evaluation information related to that evaluation dimension from multiple order evaluation information. Based on the user evaluation information, it generates corresponding evaluation summary information, thereby generating evaluation summary information corresponding to multiple evaluation dimensions.

[0130] In one specific embodiment, the preset prompt text includes prompt words corresponding to the sweetness dimension, obtains user review information related to the evaluation dimension, and generates corresponding evaluation summary information based on the user review information, which may include "The reviews repeatedly praised the taste of the sweet food in this store as great and worth trying".

[0131] In another specific embodiment, the preset prompt text includes prompt words corresponding to a time dimension. One preset prompt text includes prompt words corresponding to 7:00 AM to 9:00 AM. User review information related to this evaluation dimension is obtained, and based on the user review information, a corresponding evaluation summary is generated, which may include "The breakfast options at this shop are plentiful and taste good; it's worth trying multiple times." Another preset prompt text includes prompt words corresponding to 11:00 AM to 1:00 PM. User review information related to this evaluation dimension is obtained, and based on the user review information, a corresponding evaluation summary is generated, which may include "The lunch portions at this shop are generous and the prices are affordable."

[0132] In this embodiment of the application, by setting preset prompt text corresponding to at least one evaluation dimension, evaluation summary information corresponding to at least one evaluation dimension can be generated, thereby displaying the evaluation summary information of the corresponding evaluation dimension to users with relevant needs, so that users with relevant needs can quickly understand the overall situation of the store in the corresponding evaluation dimension.

[0133] In some embodiments of this application, adding evaluation summary information to the page information of a preset page of the target store includes: adding the evaluation summary information corresponding to each evaluation dimension to the page information of the preset page of the target store respectively, to obtain the page information corresponding to each evaluation dimension; the preset page includes the store homepage and / or the store evaluation page; and storing the mapping relationship between each evaluation dimension and the page information corresponding to each evaluation dimension.

[0134] Correspondingly, the evaluation summary information for each evaluation dimension is added to the page information of the target store's preset page. When the client sends the corresponding page information request to the server, the server will return the corresponding page information and display the preset page containing the evaluation summary information.

[0135] Store the mapping relationship between each evaluation dimension and the corresponding page information so that different page information corresponding to different evaluation dimensions can be returned to different users' clients.

[0136] In this embodiment of the application, by adding evaluation summary information corresponding to each evaluation dimension to the page information of the preset page of the target store, the evaluation summary information can be quickly displayed when the preset page is displayed. By determining the mapping relationship between each evaluation dimension and the page information corresponding to each evaluation dimension, evaluation summary information corresponding to different evaluation dimensions can be returned to the client of different users, so that users with relevant needs can quickly understand the overall situation of the store in the corresponding evaluation dimension.

[0137] In some embodiments of this application, in response to a client's access request for a preset page, sending page information to the client includes: obtaining user information of the user to which the client belongs from the client's access request for the preset page; determining the target evaluation dimension corresponding to the user based on the user information; obtaining page information corresponding to the target evaluation dimension from the mapping relationship; and sending the obtained page information to the client.

[0138] User information can be information that represents a user's identity.

[0139] It's understandable that users have preferences for products. Therefore, we can determine a user's target rating dimension based on their product preferences. When the server receives a user's access request, it retrieves the corresponding page information from the mapping relationship. This page information is then sent to the client so the user can decide whether to place an order at the target store based on the corresponding rating summary. For example, if a user likes spicy food, their target rating dimension is "spicy," and the page information corresponding to "spicy" is sent to the client.

[0140] In this embodiment of the application, by returning page information corresponding to the target evaluation dimension of the accessing user, different evaluation summary information is displayed to different users in a targeted manner, thereby providing personalized services to different users.

[0141] In some embodiments of this application, user information includes a user identifier, and each evaluation dimension includes multiple product attribute dimensions. Determining the target evaluation dimension corresponding to a user based on the user information includes: obtaining the user's historical behavior data based on the user identifier; determining the product attribute with the highest correlation to the user based on the historical behavior data; the correlation is used to characterize the frequency of the user's operations on products with product attributes in the historical behavior data; determining the product attribute dimension to which the product attribute with the highest correlation belongs from multiple product attribute dimensions; and using the determined product attribute dimension as the target evaluation dimension corresponding to the user.

[0142] The user identifier can be user contact information, user account, or other identifying information. Historical behavior data can include user actions such as browsing store pages, placing orders, and adding items to favorites within the client application. Correspondingly, the product operation frequency can be the total number of times products were ordered, browsed, and added to favorites in the historical behavior data.

[0143] In some embodiments, if the operation frequency for a certain product attribute is determined to be the highest in a user's historical behavior data, that product attribute can be identified as the product attribute with the highest relevance to the user.

[0144] In other embodiments, if the frequency of operation of at least one product attribute is greater than the operation frequency threshold in a user's historical behavior data, the at least one product attribute can be identified as the product attribute with the highest relevance to the user.

[0145] Furthermore, the product attribute with the highest relevance is determined as the user's target evaluation dimension. If there are multiple product attributes with the highest relevance and multiple target evaluation dimensions, then the evaluation summary information in the final returned page information will be of multiple types.

[0146] In this embodiment, the frequency of user operations on products corresponding to each product attribute is determined by the user's historical behavior data. The product attribute with the highest relevance to the user is then determined to identify the user's target evaluation dimension. When the user requests access to a preset page, the evaluation summary information of the target evaluation dimension is displayed, thereby achieving personalized evaluation summary information display so that the user can quickly understand the overall situation of the products that the user is interested in in the target store.

[0147] In some embodiments of this application, user information includes user access time, and each evaluation dimension includes multiple time period dimensions; determining the target evaluation dimension corresponding to the user based on user information includes: determining the time period dimension to which the user access time belongs from multiple time period dimensions; and using the determined time period dimension as the target evaluation dimension corresponding to the user.

[0148] As mentioned above, in addition to product dimensions, evaluation dimensions can also include time-based dimensions. The server can determine the target evaluation dimension for a user based on the access time of the access request.

[0149] In some embodiments, the server can divide the business hours of the target store into multiple time dimensions and generate evaluation summary information corresponding to each of the multiple time dimensions. Furthermore, the time dimension to which the time of receiving the access request belongs is determined as the target evaluation dimension, and a preset page including the evaluation summary information corresponding to the target evaluation dimension is returned to the client so that the preset page displayed by the client includes the evaluation summary information corresponding to the access time.

[0150] In addition, if a user does not have historical behavior data or the user's historical behavior data is too little to determine the corresponding product attribute dimension, the time period dimension to which the user's access time belongs can be determined, and the determined time period dimension can be used as the target evaluation dimension corresponding to the user.

[0151] In this embodiment, by determining the time period dimension to which the user's access time belongs, and using the determined time period dimension as the user's target evaluation dimension, a preset page including the evaluation summary information corresponding to the target evaluation dimension is returned to the client. This ensures that the preset page displayed on the client includes the evaluation summary information corresponding to the access time, thereby providing the user with rating summary information for different time periods, offering a reference for whether the user should place an order at different times. Furthermore, this avoids providing personalized evaluation summary information to the user when there is insufficient or no historical behavior data available.

[0152] In some embodiments of this application, the evaluation summary information includes evaluation summary text, summary basis information, and page interaction elements; the evaluation summary text is a text that summarizes and evaluates the target store, and the summary basis information is used to characterize the data on which the evaluation summary text is based.

[0153] Understandably, in order to allow users to quickly understand the overall situation of the target store, in some embodiments, the review summary information may only include the review summary text, such as... Figure 4 As shown, the review summary only includes the text "Advantages: The review repeatedly mentions that the food is crispy and flavorful, the portions are generous, and the price is very reasonable. It is a hidden gem of a shop that is worth trying."

[0154] Furthermore, to enhance the credibility of the evaluation summary information, it can also include information on the basis for the summary, such as... Figure 5As shown, the review summary includes not only the text "Advantages: The reviews repeatedly mention the crispy and rich flavor, generous portions, and affordable prices, making it a hidden gem shop worth trying," but also the information "The above is a summary of 100 real reviews."

[0155] In addition, the evaluation summary information needs to be updated in a timely manner to maintain its accuracy. Therefore, the evaluation summary information can also include interactive elements on the page, so that the model parameters of the preset content generation model can be updated based on the operations received on the interactive elements, such as... Figure 6 As shown, the page information includes review summary information. In addition to the review summary text "Advantages: The reviews repeatedly mention that the taste is crispy and rich, the overall portion is generous, and the price is very affordable. It is a treasure shop worth trying.", the summary basis information "The above is a summary of 100 real reviews", it also includes page interactive elements "like element" and "dislike element".

[0156] In summary, in addition to the evaluation summary text, the evaluation summary information may also include at least one of the following: summary basis information and page interactive elements.

[0157] After sending the page information to the client, the process also includes: receiving user interaction data returned by the client, where the user interaction data is generated by the user triggering page interaction elements in the preset page displayed on the client; and optimizing the preset content generation model based on the user interaction data corresponding to the evaluation summary information.

[0158] Among them, the user interaction data corresponding to a certain evaluation summary includes interaction data of satisfaction with the evaluation summary, i.e., the interaction data corresponding to the "like" operation, and interaction data of dissatisfaction with the evaluation summary, i.e., the interaction data corresponding to the "dislike" operation.

[0159] Further, based on the data in the user interaction data, the model parameters of the preset content generation model are adjusted to optimize the preset content generation model.

[0160] In this embodiment of the application, by setting multiple types of information in the evaluation summary information to meet the user's need to quickly understand the overall situation of the store, improve the credibility of the evaluation summary information, and provide timely feedback on the user's satisfaction with the evaluation summary information through the user's operation of the page interactive elements, the preset content generation model is optimized based on the satisfaction level, so as to improve the accuracy of the evaluation summary information output by the preset content generation model.

[0161] In some embodiments of this application, the preset content generation model is optimized based on user interaction data corresponding to the evaluation summary information, including: determining the weight value of the evaluation summary information based on multiple user interaction data corresponding to the same evaluation summary information, wherein the weight value is used to characterize the degree of positive evaluation of the evaluation summary information by the user; and adjusting the model parameters of the preset content generation model based on multiple evaluation summary information and the weight values ​​of multiple evaluation summary information.

[0162] As mentioned above, the multiple user interaction data corresponding to the same evaluation summary information include interaction data of satisfaction with the evaluation summary information, i.e., the interaction data corresponding to the "like" operation, and interaction data of dissatisfaction with the evaluation summary information, i.e., the interaction data corresponding to the "dislike" operation.

[0163] In an optional embodiment, the weight value of a certain evaluation summary information can be the number of interaction data corresponding to a like operation minus the number of interaction data corresponding to a dislike operation.

[0164] Furthermore, based on multiple evaluation summary information and their respective weight values, the model parameters of the preset content generation model are adjusted.

[0165] In an optional embodiment, a weight threshold for the evaluation summary information can be set. If the weight value of the evaluation summary information is less than the weight threshold, the evaluation summary information and its corresponding weight value, along with information related to the total evaluation price such as the order evaluation information generated from the evaluation summary information, are input into the large language model for training and learning. The preset content generation model is then trained, and the model parameters of the preset content generation model are adjusted until the weight value of the evaluation summary information is not less than the weight threshold, thus obtaining the target model parameters.

[0166] In this embodiment of the application, the weight value of the evaluation summary information is determined by multiple user interaction data corresponding to the same evaluation summary information, and the model parameters of the preset content generation model are adjusted based on the weight value to improve the accuracy of the summary evaluation information output by the preset content generation model.

[0167] In some embodiments of this application, the evaluation summary information also includes multiple evaluation label information, which are used to characterize the statistical data of positive evaluations of different evaluation dimensions in multiple order evaluation information;

[0168] Adding evaluation summary information to the page information of the target store's preset page includes: adding multiple evaluation tag information to the page information of the preset page; configuring carousel configuration information for multiple evaluation tag information in the page information, the carousel configuration information is used to characterize the speed at which the client displays each evaluation tag information in turn on the preset page.

[0169] Understandably, given the various advantages included in the evaluation summary text, it is impossible to determine the frequency of user reviews for each specific advantage, thus failing to reflect the authenticity of the evaluation summary text.

[0170] Therefore, the evaluation summary information can also include multiple evaluation tag information. Specifically, for example, if the evaluation summary text is "Advantages: The reviews repeatedly mention that the taste is crispy and rich, the overall portion is generous, and the price is very affordable, making it a treasure shop worth trying.", then the corresponding multiple evaluation tag information can be statistical data on positive reviews in terms of taste, texture, portion size, and price. For example, multiple evaluation tag information can include "87% of buyers think 'the taste is great'" and "90% of buyers think 'the price is affordable'", etc.

[0171] The carousel configuration information can include the display time of each evaluation tag and the interval between displaying different evaluation tags. The carousel configuration information can be flexibly set based on the actual situation. For example, the display time of each evaluation tag is 0.5 seconds and the interval between displaying different evaluation tags is 0.5 seconds.

[0172] Correspondingly, such as Figure 7 As shown, the review summary includes the text "Advantages: The reviews repeatedly mention the crispy and rich flavor, generous portions, and affordable price, making it a hidden gem shop worth trying.", the basis for the summary "This summary is generated from 100 real reviews," interactive elements "like" and "dislike," and the review tag "87% of buyers think 'the taste is great.'" Additionally, the review tag can include "90% of buyers think 'the price is affordable,'" etc. Multiple review tags can be displayed every 0.5 seconds, with each tag displayed for 0.5 seconds.

[0173] In an optional embodiment, the preset page includes review summary information and relevant information about the store. Taking the store's homepage as an example, the preset page includes review summary information, store name, store address, store contact information, and store rating, etc.

[0174] like Figure 8As shown, the default page is the store's homepage, which includes review summary information. This summary includes the text "Advantages: Reviews repeatedly mention its crispy and rich flavor, generous portions, and affordable price; it's a hidden gem store worth trying.", the basis for the summary "Generated from 100 genuine reviews," interactive elements "like" and "dislike," and review tag information such as "87% of buyers think 'the taste is great.'" Additionally, review tags can include "90% of buyers think 'the price is affordable,'" etc. Multiple review tags can be displayed every 0.5 seconds, with each tag displayed for 0.5 seconds.

[0175] In addition, the store page also includes store name: XXX, store address: XXX, store contact information: XXX, and store rating: XXX, among other store-related information.

[0176] In this embodiment of the application, by setting multiple evaluation tag information corresponding to the evaluation summary text, users can understand the proportion of order evaluation information corresponding to each advantage of the evaluation summary text, thereby improving the credibility of the evaluation summary text.

[0177] In some embodiments of this application, the method further includes: when the current summary period arrives, updating the evaluation summary information of the target store based on the store information of the target store and multiple order evaluation information corresponding to the current summary period, wherein the multiple order evaluation information corresponding to the current summary period includes order evaluation information newly added within the current summary period; or, when it is detected that the number of newly added order evaluation information of the target store reaches a preset threshold, updating the evaluation summary information of the target store based on the store information of the target store and multiple order evaluation information including newly added order evaluation information.

[0178] Understandably, the evaluation summary information needs to be updated regularly to ensure its accuracy. For different stores, the update can be made when the increase in user reviews reaches a certain number, or after a preset time period.

[0179] Specifically, if a store's user reviews are growing rapidly, to balance the accuracy of the review summary information with the frequency of updates, updates can be made when the store's user review volume reaches a certain amount. If a store's user review growth is slow, updates can be made after a preset time interval. Of course, the specific method for updating review summary information for a store is not fixed and can be flexibly set based on the actual situation.

[0180] The current summary period can be understood as the time interval between the current moment and the last update moment being a preset time period, which means it is time to update the evaluation summary information again.

[0181] It should be noted that when updating the evaluation summary information, it is necessary not only to update the evaluation summary information based on the newly added order evaluation information, but also to update the evaluation summary information based on the previous order evaluation information.

[0182] In this embodiment of the application, different update methods for evaluation summary information are set for different stores according to different store conditions, so as to ensure that the evaluation summary information of different stores can balance accuracy and timely update.

[0183] Figure 9 This is a flowchart illustrating an information processing method in some embodiments of this application. The method is applied to a client. Related operations on the server side in these embodiments can be referenced from the server-side operations described above. Figure 9 As shown, the method specifically includes the following steps 201-203.

[0184] Step 201: The client sends an access request to the server for the preset page of the target store.

[0185] Step 202: The client receives the page information of the preset page returned by the server.

[0186] The page information includes a summary of the target store's reviews; this summary is generated by the server based on the target store's information and multiple order reviews, and is used to represent a summary evaluation of the target store.

[0187] Step 203: Based on the page information, the client displays a preset page that includes evaluation summary information.

[0188] The access request for the preset page of the target store can be generated by the client when it receives the user's access operation for the preset page. For example, the client receives the user's touch operation on the preset page control on a certain page of the store. The preset page control can be a button, link, and toggle switch, etc.

[0189] The execution process of this application embodiment is the same as that described in the foregoing server embodiment, and will not be repeated here.

[0190] The client receives a user's request to view a preset page, generates an access request for that page, and sends it to the server. Upon receiving the request, the server sends the preset page information, including review summaries, back to the client. The client then displays this preset page, showing the target store's review summary as a reference for the user to decide whether to place an order.

[0191] In some embodiments of this application, the preset page includes the homepage or review page of the target store; the review summary information includes review summary text, summary basis information, and page interaction elements; after displaying the preset page including the review summary information, the method further includes: detecting that the page interaction elements displayed on the preset page are triggered, obtaining user interaction data submitted by the user through the page interaction elements; and sending the user interaction data to the server.

[0192] like Figure 4-6 As shown, the evaluation summary information may only include the evaluation summary text, while the total evaluation price information may include, in addition to the evaluation summary text, at least one of the following: summary basis information and page interactive elements.

[0193] like Figure 6 As shown, the page has interactive elements "like" and "dislike". When the interactive element "like" is triggered, interactive data indicating satisfaction with the evaluation summary information is sent to the server. When the interactive element "dislike" is triggered, interactive data indicating dissatisfaction with the evaluation summary information is sent to the server.

[0194] Specifically, detecting that a page interaction element displayed on a preset page has been triggered can be achieved by detecting that a page interaction element has been triggered at a certain moment, and that the page interaction element has not been triggered again within a preset time period.

[0195] If a page interaction element is detected to be triggered at a certain moment, and is triggered again within a preset time period, the operation of triggering the page interaction element is cancelled, and the corresponding interaction data is not sent to the server.

[0196] In this embodiment of the application, by setting multiple types of information in the evaluation summary information to meet the user's need to quickly understand the overall situation of the store, improve the credibility of the evaluation summary information, and provide timely feedback on the user's satisfaction with the evaluation summary information through the user's operation of the page interactive elements, the preset content generation model is optimized based on the satisfaction level, so as to improve the accuracy of the evaluation summary information output by the preset content generation model.

[0197] In some embodiments of this application, the evaluation summary information further includes multiple evaluation tag information; based on the page information, a preset page including the evaluation summary information is displayed, which further includes: based on the carousel configuration information configured in the page information, multiple evaluation tag information is carouseled in the displayed preset page, and the carousel configuration information is used to characterize the speed at which the client displays each evaluation tag information in turn in the preset page.

[0198] It is understandable that, regarding the various advantages included in the evaluation summary text, it is impossible to determine the specific percentage of order evaluation information corresponding to each advantage, leading users to be uncertain about the authenticity of the evaluation summary text.

[0199] Therefore, the evaluation summary information can also include multiple evaluation tag information. Specifically, for example, if the evaluation summary text is "Advantages: The reviews repeatedly mention that the taste is crispy and rich, the overall portion is generous, and the price is very affordable, making it a treasure shop worth trying.", then the corresponding multiple evaluation tag information can be statistical data on positive reviews in terms of taste, texture, portion size, and price. For example, multiple evaluation tag information can include "87% of buyers think 'the taste is great'" and "90% of buyers think 'the price is affordable'", etc.

[0200] The carousel configuration information includes the display time of each evaluation tag and the interval between displaying different evaluation tags. The carousel configuration information can be flexibly set according to the actual situation. For example, the display time of each evaluation tag is 0.5 seconds and the interval between displaying different evaluation tags is 0.5 seconds.

[0201] In this embodiment of the application, by setting multiple evaluation tag information corresponding to the evaluation summary text, users can understand the proportion of order evaluation information corresponding to each advantage of the evaluation summary text, thereby improving the credibility of the evaluation summary text.

[0202] Corresponding to the embodiments of the aforementioned information processing methods, other embodiments of this application also provide an information processing system, such as... Figure 1 As shown, the system includes a server and a client;

[0203] The server-side component generates a summary evaluation of the target store based on its store information and multiple order reviews. This summary evaluation is used to represent a generalized assessment of the target store. The server then adds the summary evaluation to the page information of a preset page for the target store. In response to a client's request to access the preset page, the server sends the page information to the client.

[0204] The client is used to send access requests for a preset page to the server; receive page information for the preset page returned by the server; and display the preset page, including evaluation summary information, based on the page information.

[0205] The information processing system and the information processing method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0206] Corresponding to the embodiments of the aforementioned information processing method, this application also provides embodiments of an information processing apparatus.

[0207] Figure 10This is a schematic diagram illustrating the structure of an information processing apparatus according to an exemplary embodiment. The apparatus is used to execute the information processing method applied to a first terminal provided in any of the above embodiments, such as... Figure 10 As shown, the information processing device includes:

[0208] The generation module 1001 is used to generate evaluation summary information of the target store based on the store information of the target store and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store.

[0209] Add module 1002, used to add the evaluation summary information to the page information of the preset page of the target store;

[0210] The sending module 1003 is used to send the page information to the client in response to the client's access request for the preset page, so that the client displays the preset page containing the evaluation summary information.

[0211] In some embodiments, the generation module 1001 is specifically used for:

[0212] Based on the store information of the target store, determine the store category to which the target store belongs;

[0213] Retrieve the preset prompt text corresponding to the store category;

[0214] Based on the store information, the multiple order review information, and the preset prompt text, the review summary information of the target store is generated through a preset content generation model.

[0215] In some embodiments, the preset prompt text includes prompt words corresponding to at least one evaluation dimension; the generation module 1001 is further specifically used for:

[0216] The store information, the multiple order review information, and the prompt words for each review dimension in the preset prompt text are input into the preset content generation model, and the model outputs the review summary information corresponding to each review dimension.

[0217] In some embodiments, module 1002 is added, specifically for:

[0218] The evaluation summary information corresponding to each evaluation dimension is added to the page information of the preset page of the target store to obtain the page information corresponding to each evaluation dimension; the preset page includes the store homepage and / or the store evaluation page.

[0219] Store the mapping relationship between each evaluation dimension and the page information corresponding to each evaluation dimension.

[0220] In some embodiments, the sending module 1003 is specifically used for:

[0221] Obtain user information of the user to whom the client belongs from the client's access request for the preset page;

[0222] Based on the user information, the target evaluation dimension corresponding to the user is determined;

[0223] From the mapping relationship, obtain the page information corresponding to the target evaluation dimension;

[0224] The obtained page information is sent to the client.

[0225] In some embodiments, the user information includes a user identifier, and each evaluation dimension includes multiple product attribute dimensions; the sending module 1003 is further specifically used for:

[0226] Based on the user identifier, obtain the user's historical behavior data;

[0227] Based on the historical behavior data, the product attribute with the highest correlation to the user is determined; the correlation is used to characterize the frequency of the user's operations on products with the product attribute in the historical behavior data;

[0228] From the multiple product attribute dimensions, determine the product attribute dimension to which the product attribute with the highest correlation belongs;

[0229] The determined product attribute dimensions will be used as the target evaluation dimensions for the user.

[0230] In some embodiments, the user information includes user access time, and the evaluation dimensions include multiple time period dimensions; the sending module 1003 is further specifically used for:

[0231] From the multiple time period dimensions, determine the time period dimension to which the user's access time belongs;

[0232] The determined time period dimension will be used as the target evaluation dimension for the user.

[0233] In some embodiments, the evaluation summary information includes evaluation summary text, summary basis information, and page interaction elements; the evaluation summary text is a text that provides a summary evaluation of the target store, and the summary basis information is used to characterize the data on which the evaluation summary text is based;

[0234] After sending the page information to the client, the system further includes a receiving module and an optimization processing module.

[0235] The receiving module is used to receive user interaction data returned by the client, wherein the user interaction data is generated by the user triggering the page interaction elements in the preset page displayed by the client;

[0236] The optimization processing module is used to optimize the preset content generation model based on the user interaction data corresponding to the evaluation summary information.

[0237] In some embodiments, the optimization processing module is specifically used for:

[0238] Based on multiple user interaction data corresponding to the same evaluation summary information, a weight value for the evaluation summary information is determined, and the weight value is used to characterize the degree of positive evaluation of the evaluation summary information by the user.

[0239] Based on multiple evaluation summary information and the weight values ​​of the multiple evaluation summary information, the model parameters of the preset content generation model are adjusted.

[0240] In some embodiments, the evaluation summary information further includes multiple evaluation tag information, which is used to characterize statistical data on positive evaluations of different evaluation dimensions in the multiple order evaluation information;

[0241] Module 1002 is also specifically used for:

[0242] Add the multiple evaluation tag information to the page information of the preset page;

[0243] The page information is configured with carousel configuration information for the multiple evaluation tags, which is used to characterize the speed at which the client displays each evaluation tag in turn on the preset page.

[0244] In some embodiments, the above-described apparatus further includes an update module, which is used to:

[0245] When the current summary period arrives, based on the target store's store information and multiple order review information corresponding to the current summary period, update the target store's review summary information, where the multiple order review information corresponding to the current summary period includes order review information newly added within the current summary period; or,

[0246] If the number of newly added order review information for the target store reaches a preset threshold, the review summary information of the target store is updated based on the store information of the target store and multiple order review information, including the newly added order review information.

[0247] The information processing apparatus and the information processing method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0248] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0249] Figure 11 This is a schematic diagram illustrating the structure of an information processing apparatus according to an exemplary embodiment. The apparatus is used to execute the information processing method applied to a server provided in any of the above embodiments, such as... Figure 11 As shown, the information processing device includes:

[0250] The sending module 1101 is used to send an access request for a preset page of the target store to the server.

[0251] The receiving module 1102 is used to receive page information of the preset page returned by the server. The page information includes the evaluation summary information of the target store. The evaluation summary information is generated by the server based on the store information of the target store and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store.

[0252] Display module 1103 is used to display the preset page including the evaluation summary information based on the page information.

[0253] In some embodiments, the preset page includes the target store's homepage or store review page; the review summary information includes review summary text, summary basis information, and page interactive elements;

[0254] The above-mentioned device further includes: an acquisition module, used for:

[0255] The system detects that the page interaction element displayed on the preset page has been triggered, and obtains the user interaction data submitted by the user through the page interaction element.

[0256] The sending module 1101 is also used to: send the user interaction data to the server.

[0257] In some embodiments, the evaluation summary information also includes multiple evaluation tag information;

[0258] Display module 1103 is also used for:

[0259] Based on the carousel configuration information configured in the page information, the multiple evaluation tag information is carouseled on the preset page. The carousel configuration information is used to characterize the speed at which the client displays each of the evaluation tag information in turn on the preset page.

[0260] The information processing apparatus and the information processing method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0261] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0262] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components illustrated as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0263] This application also provides an electronic device corresponding to the information processing method provided in the foregoing embodiments, for executing the aforementioned information processing method.

[0264] Figure 12 The present invention illustrates a hardware structure diagram of an electronic device according to an exemplary embodiment. The electronic device includes a communication interface 601, a processor 602, a memory 603, and a bus 604. The communication interface 601, processor 602, and memory 603 communicate with each other via the bus 604. The processor 602 can execute the information processing method described above by reading and executing machine-executable instructions corresponding to the control logic of the information processing method in the memory 603. The specific content of this method is described in the above embodiment and will not be repeated here.

[0265] The memory 603 mentioned in this embodiment can be any electronic, magnetic, optical, or other physical storage device, and can contain stored information such as executable instructions, data, etc. Specifically, the memory 603 can be RAM (Random Access Memory), flash memory, storage drive (such as hard disk drive), any type of storage disk (such as optical disc, DVD, etc.), or similar storage media, or combinations thereof. Communication between this system network element and at least one other network element is achieved through at least one communication interface 601 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc., can be used.

[0266] Bus 604 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 603 is used to store programs, and the processor 602 executes the programs after receiving execution instructions.

[0267] Processor 602 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 602 or by instructions in software form. The processor 602 can be a general-purpose processor, including a network processor (NP), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware controls, etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor.

[0268] The electronic devices and information processing methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0269] This application also provides a computer-readable storage medium corresponding to the information processing method provided in the foregoing embodiments, such as... Figure 13 As shown, the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the information processing method provided in any of the foregoing embodiments.

[0270] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0271] The computer-readable storage medium provided in the above embodiments of this application and the information processing method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0272] This application also provides a computer program product corresponding to the information processing method provided in the foregoing embodiments. The computer program product includes a computer program that is executed by a processor to implement the information processing method provided in the foregoing embodiments.

[0273] The computer program products provided in the above embodiments of this application and the information processing methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0274] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0275] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0276] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An information processing method, characterized in that, Applied to the server side, the method includes: Based on the store information and multiple order review information of the target store, a review summary information of the target store is generated. The review summary information is used to represent a summary evaluation of the target store. Add the evaluation summary information to the page information of the preset page of the target store; In response to a client's access request for the preset page, the page information is sent to the client so that the client displays the preset page containing the evaluation summary information.

2. The method according to claim 1, characterized in that, The process of generating a summary of reviews for the target store based on its store information and multiple order reviews includes: Based on the store information of the target store, determine the store category to which the target store belongs; Retrieve the preset prompt text corresponding to the store category; Based on the store information, the multiple order review information, and the preset prompt text, the review summary information of the target store is generated through a preset content generation model.

3. The method according to claim 2, characterized in that, The preset prompt text includes prompt words corresponding to at least one evaluation dimension; the step of generating the evaluation summary information of the target store based on the store information, the multiple order evaluation information, and the preset prompt text, through a preset content generation model, includes: The store information, the multiple order review information, and the prompt words for each review dimension in the preset prompt text are input into the preset content generation model, which outputs the review summary information corresponding to each review dimension.

4. The method according to claim 3, characterized in that, Adding the evaluation summary information to the page information of the target store's preset page includes: The evaluation summary information corresponding to each evaluation dimension is added to the page information of the preset page of the target store to obtain the page information corresponding to each evaluation dimension; the preset page includes the store homepage and / or the store evaluation page. Store the mapping relationship between each evaluation dimension and the page information corresponding to each evaluation dimension.

5. The method according to claim 4, characterized in that, The step of sending the page information to the client in response to the client's access request for the preset page includes: Obtain user information of the user to whom the client belongs from the client's access request for the preset page; Based on the user information, the target evaluation dimension corresponding to the user is determined; From the mapping relationship, obtain the page information corresponding to the target evaluation dimension; The obtained page information is sent to the client.

6. The method according to claim 5, characterized in that, The user information includes a user identifier, and each evaluation dimension includes multiple product attribute dimensions; determining the target evaluation dimension corresponding to the user based on the user information includes: Based on the user identifier, obtain the user's historical behavior data; Based on the historical behavior data, the product attribute with the highest correlation to the user is determined; the correlation is used to characterize the frequency of the user's operations on products with the product attribute in the historical behavior data; From the multiple product attribute dimensions, determine the product attribute dimension to which the product attribute with the highest correlation belongs; The determined product attribute dimensions will be used as the target evaluation dimensions for the user.

7. The method according to claim 5, characterized in that, The user information includes the user's access time, and each evaluation dimension includes multiple time period dimensions; determining the target evaluation dimension corresponding to the user based on the user information includes: From the multiple time period dimensions, determine the time period dimension to which the user's access time belongs; The determined time period dimension will be used as the target evaluation dimension for the user.

8. The method according to any one of claims 2-7, characterized in that, The evaluation summary information includes evaluation summary text, summary basis information, and page interaction elements; the evaluation summary text is a text that summarizes and evaluates the target store, and the summary basis information is used to represent the data on which the evaluation summary text is based; After sending the page information to the client, the process further includes: Receive user interaction data returned by the client, wherein the user interaction data is generated by the user triggering the page interaction elements in the preset page displayed by the client; Based on the user interaction data corresponding to the evaluation summary information, the preset content generation model is optimized.

9. An information processing method, characterized in that, Applied to a client, the method includes: Send an access request to the server for the preset page of the target store; The server receives page information of the preset page returned by the server. The page information includes the evaluation summary information of the target store. The evaluation summary information is generated by the server based on the store information of the target store and multiple order evaluation information. The evaluation summary information is used to represent a summary evaluation of the target store. Based on the page information, the preset page including the evaluation summary information is displayed.

10. The method according to claim 9, characterized in that, The preset page includes the target store's homepage or store review page; the review summary information includes review summary text, summary basis information, and page interactive elements; After displaying the preset page including the evaluation summary information, the system further includes: The system detects that the page interaction element displayed on the preset page has been triggered, and obtains the user interaction data submitted by the user through the page interaction element. The user interaction data is sent to the server.

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