Video recommendation method, device, electronic device and medium

Abstract

The present disclosure provides a video recommendation method, device, electronic device and medium, which relate to the field of artificial intelligence technology, especially to the fields of big data, intelligent recommendation, large models and deep learning. The method includes: obtaining at least two candidate videos that match the target object; determining a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video; obtaining a semantic interest tree pre-constructed for the target object; wherein the semantic interest tree is constructed according to input operations corresponding to historical videos and a second semantic code group corresponding to historical videos, and the historical videos are videos that the target object has viewed; based on the first semantic code group and the semantic interest tree, determining the recommendation order between the candidate videos; and performing the video recommendation operation according to the recommendation order. The present disclosure effectively improves the accuracy and personalization of video recommendations, and using the present disclosure for video recommendation is conducive to improving user experience.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, particularly to big data, intelligent recommendation, large models, and deep learning, and specifically to a video recommendation method. Background Art

[0002] Video recommendation systems predict users' interactions with videos and recommend videos that users may be interested in. Video recommendation systems also need to explore new user interests.

[0003] Currently, most related technical solutions explore and mine new user interests based on the vertical category to which a video belongs. However, the concept of vertical category is broad, and a single vertical category encompasses a wide range of sub-sectors, making it difficult to effectively represent user interests. Therefore, mining new user interests based on vertical category granularity is not ideal. Summary of the Invention

[0004] The present disclosure provides a video recommendation method, device, electronic device, and medium.

[0005] According to one aspect of the present disclosure, a video recommendation method is provided, the method comprising:

[0006] Obtain at least two candidate videos that match the target object;

[0007] Determining a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video;

[0008] Obtaining a pre-built semantic interest tree for the target object; wherein the semantic interest tree is built based on an input operation corresponding to a historical video and a second semantic code group corresponding to the historical video, the historical video being a video that the target object has viewed;

[0009] Determining a recommendation order among the candidate videos based on the first semantic code group and the semantic interest tree;

[0010] The video recommendation operation is performed according to the recommendation order.

[0011] According to another aspect of the present disclosure, a video recommendation device is provided, the device comprising:

[0012] A candidate video acquisition module is used to acquire at least two candidate videos that match the target object;

[0013] A semantic code group determination module, configured to determine a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video;

[0014] An interest tree acquisition module is configured to acquire a pre-built semantic interest tree for the target object; wherein the semantic interest tree is constructed based on input operations corresponding to historical videos and second semantic code groups corresponding to the historical videos, wherein the historical videos are videos that the target object has viewed;

[0015] A recommendation order determination module, configured to determine a recommendation order among the candidate videos based on the first semantic code group and the semantic interest tree;

[0016] The video recommendation module is used to perform a video recommendation operation according to the recommendation order.

[0017] According to another aspect of the present disclosure, an electronic device is provided, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the video recommendation method described in any embodiment of the present disclosure.

[0021] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the video recommendation method described in any embodiment of the present disclosure.

[0022] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the video recommendation method according to any embodiment of the present disclosure.

[0023] This disclosure effectively improves the accuracy and personalization of video recommendations, and using this disclosure for video recommendations is conducive to improving user experience.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention.

[0026] Figure 1 is a flowchart of a video recommendation method provided according to an embodiment of the present disclosure;

[0027] Figure 2is a flowchart of a first semantic code group determination solution provided according to an embodiment of the present disclosure;

[0028] Figure 3 is a flowchart of another video recommendation method provided according to an embodiment of the present disclosure;

[0029] Figure 4 is a structural diagram of a semantic interest tree provided according to an embodiment of the present disclosure;

[0030] Figure 5 is a flowchart of another video recommendation method provided according to an embodiment of the present disclosure;

[0031] Figure 6 is a structural diagram of a video recommendation device provided according to an embodiment of the present disclosure;

[0032] Figure 7 A block diagram of an electronic device for implementing the video recommendation method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0034] Figure 1 This is a flow chart of a video recommendation method provided according to an embodiment of the present disclosure. The embodiment of the present disclosure can be applied to the case of recommending videos to users. The method can be executed by a video recommendation device, which can be implemented in software and / or hardware. Figure 1 As shown, the video recommendation method of this embodiment may include:

[0035] S101: Obtain at least two candidate videos that match the target object.

[0036] S102: Determine a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video.

[0037] S103, obtaining a pre-built semantic interest tree for the target object; wherein the semantic interest tree is built according to input operations corresponding to historical videos and second semantic code groups corresponding to the historical videos, and the historical videos are videos that the target object has viewed.

[0038] S104: Determine a recommendation order among the candidate videos based on the first semantic code group and the semantic interest tree.

[0039] S105: Execute a video recommendation operation according to the recommendation order.

[0040] The candidate videos matching the target subject are videos that match the target subject's interests and are to be recommended to the target subject. The candidate videos can be videos carefully ranked by a video recommendation system. There are at least two candidate videos, and the specific number of candidate videos is determined based on actual business needs and is not limited here. The disclosed technical solution can sort candidate videos, such as those carefully ranked by a video recommendation system, to recommend videos that better match the target subject's interests.

[0041] Each candidate video has a corresponding first semantic code group. The first semantic code group is the semantic representation of the candidate video at different levels of abstraction, which can describe at least two levels of semantics of the candidate video. These levels can be understood as semantic divisions from concrete to abstract, from details to the whole. Low-level semantics may correspond to specific objects, actions or scenes in the candidate video, while high-level semantics may correspond to the theme, category or emotion of the candidate video. Optionally, the first semantic code group of the candidate video is obtained by hierarchically quantifying the video content of the candidate video. The vertical category to which the candidate video belongs will be considered in the generation process of the first semantic code group, but it is not the level itself.

[0042] Among them, the semantic interest tree is constructed based on the input operations corresponding to the historical videos and the second semantic code group corresponding to the historical videos. The historical videos are videos that the target object has browsed. The historical videos are associated with input operations. Input operations refer to the interactive behaviors and feedback behaviors exhibited by the target object in the process of browsing the videos. Optionally, the input operations include: viewing operations and interactive operations. The input operations corresponding to the historical videos can reflect the target object's interest level in the historical videos. The acquisition and application of user-related information such as the input operations of the target object involved in the embodiments of the present disclosure are known and agreed to by the user, are in compliance with the provisions of relevant laws and regulations, and do not violate public order and good morals.

[0043] It can be understood that the semantic interest tree is dynamically adjusted and optimized according to the actual interest points of the target object, and the semantic interest tree is continuously improved with the further accumulation and analysis of input operations.

[0044] The second semantic code group is used to describe at least two levels of semantics of the historical video. The semantic interest tree constructed based on the input operation corresponding to the historical video and the second semantic code group corresponding to the historical video can reflect the semantics of interest to the target object in a hierarchical manner.

[0045] The first semantic code group describes at least two levels of semantics for the candidate videos, and the semantic interest tree hierarchically describes the semantics of interest to the target object. Optionally, the at least two levels of semantics described by the first semantic code group are matched with the multiple levels of semantics described by the semantic interest tree, and a recommendation order among the candidate videos is determined based on the matching results. The video recommendation operation is performed according to the recommendation order.

[0046] The disclosed technical solution pre-constructs a semantic interest tree for a target object, using it to describe the multi-level semantics of interest to the target object. A first semantic code group is used to describe at least two levels of semantics for candidate videos. Based on the semantic interest tree and the first semantic code group, a recommendation order is determined for each candidate video. This solution fully exploits the relationship between video semantics and user interests, enabling video recommendations based on multi-level semantics. This effectively improves the accuracy and personalization of video recommendations, thereby enhancing the user experience.

[0047] In an optional embodiment, determining the first semantic code group corresponding to the candidate video includes: determining the semantic fusion vector of the candidate video based on the text description and video frame description of the candidate video; calculating the semantic similarity between the semantic fusion vector and the candidate semantic vector in a preset semantic code table; wherein the preset semantic code table is trainable; based on the semantic similarity, determining a target semantic vector in the candidate semantic vector that is semantically similar to the semantic fusion vector; identifying the position of the target semantic vector in the preset semantic code table, and determining it as the first semantic codeword in the first semantic code group; calculating the residual between the semantic fusion vector and the target semantic vector, and using the residual as a new semantic fusion vector.

[0048] Among them, the text description of the candidate video refers to the text used for video semantics. The text description of the candidate video can be the video theme, content overview and video subtitles of the candidate video. The video frame description of the candidate video refers to the video frame used to describe the video semantics. The video frame description of the candidate video can be a video frame captured from the candidate video. For example, the video frame of the first 5 seconds of the candidate video is captured to determine the video frame description of the candidate video. The text description of the candidate video is in text mode, and the video frame description of the candidate video is in image mode. Optionally, the text description and video frame description of the candidate video are vectorized respectively to obtain a text semantic vector and a visual semantic vector. The text semantic vector and the visual semantic vector are fused to obtain a semantic fusion vector of the candidate video. Optionally, the text semantic vector and the visual semantic vector are spliced, and the splicing result is used as a semantic fusion vector.

[0049] The semantic fusion vector of the candidate video includes both the textual semantics and the visual semantics of the candidate video.

[0050] The preset semantic code table is used to quantize the continuous semantic fusion vector into discrete codewords. These codewords are candidate semantic vectors from the preset semantic code table. The codewords in the preset semantic code table are not fixed and are optimized and updated based on the training data and the model's loss function.

[0051] Semantic similarity is used to quantify the semantic proximity between the semantic fusion vector and the candidate semantic vector. Optionally, the semantic similarity is cosine similarity.

[0052] By calculating the semantic similarity between the semantic fusion vector and the candidate semantic vectors in the preset semantic code table, the semantic fusion vector can be mapped to the semantically closest codeword in the preset semantic code table. Optionally, the preset semantic code table is maintained by an RQ-VAE (Residual Quantized Variational Autoencoder) model. The target semantic vector refers to the codeword in the preset semantic code table that is semantically closest to the semantic fusion vector. The position of the target semantic vector in the preset semantic code table is identified and determined as the first semantic codeword in the first semantic code group.

[0053] The residual between the semantic fusion vector and the target semantic vector is used to represent the degree of approximation or information loss between the semantic fusion vector and the target semantic vector. Repeating the above calculation using the residual as the new semantic fusion vector can repeatedly obtain the closest codeword, that is, the position identifier of the target semantic vector in the preset semantic code table. These position identifiers constitute the first semantic code group, and each position identifier corresponds to a first semantic codeword in the first semantic code group.

[0054] Figure 2 is a flowchart of a first semantic code group determination solution provided according to an embodiment of the present disclosure; see Figure 2 , to generate text semantic vectors from the text description of the candidate video, generate visual semantic vectors from the video frame description of the candidate video, and splice the text semantic vectors and visual semantic vectors to obtain the semantic fusion vector of the candidate video corresponding to Figure 2 Embedding in.

[0055] The semantic fusion vector is passed through a DNN encoder (Deep Neural Networks, deep neural network) Figure 2 The Encoder in is encoded to generate a new embedding vector, represented as e∈R 1xN , N represents the embedding vector dimension. The RQ-VAE model maintains a trainable preset semantic code table C∈R MxN , corresponding to Figure 2codebook_1, codebook_2 and codebook_3 in the preset semantic code table. M represents the length of the preset semantic code table. RQ-VAE will calculate the cosine similarity with e in the M dimension of the preset semantic code table. Based on the cosine similarity, it selects the codeword closest to the semantic fusion vector in the preset semantic code table, that is, the target semantic vector, and counts the target semantic vector as C i ∈R 1xN Where i represents the position identifier of the target semantic vector in the preset semantic code table.

[0056] Furthermore, through eC i The residual e1 can be obtained. Then, the above calculation is repeated using the residual e1. By repeating the calculation, the closest codeword, that is, the position identifier of the target semantic vector in the preset semantic code table, can be obtained multiple times. These position identifiers constitute the first semantic code group. Figure 2 In the (7, 1, 4) in [1], each position identifier corresponds to a first semantic codeword in the first semantic codegroup. RQ-VAE is pre-trained, and the MSE loss between its final embedding representation and the original embedding representation is as small as possible. The final embedding representation generated by RQ-VAE is obtained by adding the candidate semantic vectors corresponding to each first semantic codeword in the first semantic codegroup and then decoding it through the DNN decoder. The original embedding representation is the semantic fusion vector encoded by the DNN encoder.

[0057] The above technical solution provides a feasible solution for determining the first semantic code group, provides technical support for describing at least two levels of semantics of candidate videos, and provides data support for video recommendation based on multi-level semantics.

[0058] In an optional embodiment, the semantic node in the semantic interest tree corresponds to the second semantic codeword in the second semantic code group, and the level of the semantic node in the semantic interest tree corresponds to the position of the second semantic codeword in the second semantic code group; the interest weight associated with the semantic node is determined based on the input operation corresponding to the historical video.

[0059] The second semantic code group corresponds to the historical video, and the second semantic code group includes at least two second semantic code words, and one second semantic code word corresponds to a semantic level. The second semantic code group is used to describe at least two levels of semantics of the historical video. The position of the second semantic code word in the second semantic code group is used to describe the relative relationship between the semantic levels represented by the second semantic code word. The semantic interest tree includes at least one semantic node, and one semantic node corresponds to a semantic level. The semantic interest tree includes at least two semantic nodes, and the level of the semantic node in the semantic interest tree is determined according to the relative relationship between the semantic levels represented by the semantic node.

[0060] The input operation corresponding to the historical video can reflect the target object's interest in the historical video. The interest weight is used to quantify the target object's interest in the semantic level corresponding to the semantic node.

[0061] The semantic nodes in the semantic interest tree correspond to the second semantic codewords in the second semantic code group. Each historical video has a corresponding second semantic codeword, and the interest weight associated with the semantic node is determined based on the input operation corresponding to the historical video. The semantic interest tree represents the relationship between video semantics and user interests.

[0062] The above technical solution provides a feasible solution for constructing a semantic interest tree, provides technical support for hierarchically describing the semantics of interest to the target object, quantifies the correlation between video semantics and user interests, and provides data support for video recommendation based on multi-level semantics.

[0063] In an optional embodiment, based on the first semantic code group and the semantic interest tree, a recommendation order among the candidate videos is determined, including: determining the recommendation order based on the first semantic codeword in the first semantic code group, the semantic nodes in the semantic interest tree, and the interest weights associated with the semantic nodes.

[0064] The first semantic code group includes at least two first semantic codewords, and each first semantic codeword describes a level of semantics of the candidate video.

[0065] The semantic interest tree includes at least two semantic nodes. The semantic interest tree describes the multi-level semantics that the target object is interested in. The interest weight is used to quantify the target object's interest in the semantic level corresponding to the semantic node.

[0066] Optionally, the first semantic codeword in the first semantic code group is matched with a semantic node in the semantic interest tree to determine the semantic node that matches the first semantic codeword. The target subject's interest in the candidate videos is then determined based on the interest weight associated with the semantic node. Then, a recommendation order is determined among the candidate videos based on the target subject's interest in the candidate videos.

[0067] The above technical solution provides a practical solution for determining the recommendation order, which can be used to determine the recommendation order among candidate videos, and uses the association between video semantics and user interests for video recommendation, providing technical support for realizing video recommendation based on multi-level semantics.

[0068] Figure 3 This is a flowchart of another video recommendation method provided according to an embodiment of the present disclosure; this embodiment is an optional solution proposed based on the above embodiment.

[0069] See also Figure 3 The video recommendation method provided in this embodiment includes:

[0070] S301 , in response to determining that the second semantic code group includes a second semantic codeword corresponding to the semantic node, determining the interest tendency information and browsing duration information of the target object for the historical video based on the input operation.

[0071] The second semantic code group corresponding to the historical video includes the second semantic codeword corresponding to the semantic node, indicating that the historical video hits the semantics of interest to the target object. In other words, the semantics of interest to the target object are viewed again by the target object. At this time, the interest weight associated with the semantic node is reconfigured.

[0072] Input operations corresponding to historical videos can reflect the target subject's level of interest in the historical videos. Based on interest tendency information, historical videos can be divided into those that the target subject is interested in and those that are not. Interest tendency information can be determined based on the target subject's input operations related to historical videos. For example, if the target subject watches the video for a long time and engages in interactive behaviors such as liking, adding to favorites, and sharing, it can be determined that the target subject is interested in the historical video. Conversely, the target subject is not interested in the historical video.

[0073] The time spent by the target object in browsing the historical videos can be determined based on the browsing time information. Generally, the browsing time is proportional to the target object's interest in the historical videos.

[0074] S302: Based on the interest tendency information, determine a target component to be adjusted in the weight components of the interest weight.

[0075] The target component is the weight component in the interest weight that needs to be adjusted. Optionally, the weight component of the interest weight includes a positive weight and a negative weight. Optionally, if it is determined based on the interest tendency information that the target object is interested in the historical video, the positive weight of the interest weight is determined as the target component to be adjusted; if it is determined based on the interest tendency information that the target object is not interested in the historical video, the negative weight of the interest weight is determined as the target component to be adjusted.

[0076] S303: Determine a target step size for adjusting the target component based on the browsing duration information and the level of the semantic node corresponding to the second semantic codeword in the semantic interest tree.

[0077] The target step size refers to the step size used when reconfiguring the interest weights of semantic nodes.

[0078] The hierarchy of semantic nodes in the semantic interest tree, from shallow to deep, can be understood as moving from concrete to abstract, from detail to the whole. The deeper a semantic node is in the semantic interest tree, the more abstract the semantics it represents, and the greater the probability of belonging to a higher-level semantics. For semantic nodes corresponding to higher-level semantics, a larger step size can be used to adjust their interest weights.

[0079] When analyzing a target's interest in historical videos, viewing duration is also an important indicator. Longer viewing duration indicates a greater level of interest. For semantic nodes with longer viewing durations, a larger step size can be used to adjust their interest weights.

[0080] S304: Reconfigure the weight of the semantic node corresponding to the second semantic codeword based on the target step size and the target component.

[0081] When the target step size, target component, and semantic nodes that need weight adjustment are determined, the target step size is used to reconfigure the weight of the target component associated with the semantic node.

[0082] Optionally, after the weight of the semantic node corresponding to the second semantic codeword is adjusted, the weight adjustment time of the semantic node is recorded.

[0083] The disclosed technical solution determines the timing of weight adjustment as the second semantic codeword corresponding to the semantic node included in the second semantic code group, that is, when the semantics of interest to the target object is browsed again by the target object. According to the interest tendency information corresponding to the historical video, the target component to be adjusted in the interest weight is determined; according to the browsing time information corresponding to the historical video and the level of the semantic node corresponding to the second semantic codeword in the semantic interest tree, the target step size used when adjusting the weight of the target component is designed, and based on the target step size and the target component, the interest weight associated with the semantic node is adjusted. In this way, the interest changes of the target object can be quickly captured, and the semantic interest tree can be adjusted and updated in a timely manner, so that the semantic interest tree can more accurately reflect the current interest status of the target object.

[0084] In an optional embodiment, the method further includes: determining the weight values ​​of the positive component and the negative component in the interest weight corresponding to the semantic node within a first preset time period based on the weight adjustment time corresponding to the semantic node; determining the weight difference between the weight value of the positive component and the weight value of the negative component; in response to determining that the weight difference is greater than a preset difference threshold, determining the semantic node as an invalid node; and removing the invalid node from the semantic interest tree.

[0085] The weight adjustment time refers to the time for adjusting the interest weight of the semantic node. The weight adjustment time corresponding to the semantic node can be used to determine which semantics the target object browsed within the first preset time period.

[0086] The first preset time period is a period that is relatively close to the current time period. This helps improve the timeliness of the semantic interest tree and enables it to promptly reflect the target object's recent interest changes. The specific value of the first preset time period is determined according to actual business needs. For example, the first preset time period can be the last 12 hours.

[0087] The positive component and the negative component are weight components in the interest weight, corresponding to the target object's interest and disinterest in the semantics corresponding to the semantic node, respectively.

[0088] The weight values ​​of the positive component and the negative component in the interest weight corresponding to the semantic node in the first preset time period are used to quantify the target object's interest and lack of interest in the semantics corresponding to the semantic node in the first preset time period.

[0089] The difference between the weight values ​​of the positive component and the negative component can be used to determine the target object's recent interest changes. A preset difference threshold is used to determine whether the target object is still interested in the semantics corresponding to the semantic node. The specific value of the preset difference threshold is determined based on actual business needs and is not limited here.

[0090] If the weight difference is greater than the preset difference threshold, it means that the target object's interest has changed within the first preset time period and is no longer interested in the semantics corresponding to the semantic node. Then the semantic node is determined to be an invalid node and the invalid node is removed from the semantic interest tree.

[0091] Figure 4 This is a structural diagram of a semantic interest tree provided according to an embodiment of the present disclosure. Figure 4 , Figure 4Figure 1 shows a semantic interest tree with four semantic levels. The semantic nodes in this tree include E, E_F, E_F_G, and E_F_G_H. These are located at Layer 1, Layer 2, Layer 3, and Layer 4 of the semantic interest tree, respectively. The values ​​0.8, 0.7, 0.5, and 0.3 in the semantic nodes represent the interest weights associated with E, E_F, E_F_G, and E_F_G_H, respectively. Figure 4 The semantic nodes shown with dotted lines are invalid nodes and need to be removed from the semantic interest tree.

[0092] The above technical solution provides a practical and feasible interest tree optimization scheme. By subtracting the weight values ​​of the positive and negative components of the interest weights corresponding to semantic nodes within a first preset time period and comparing the resulting weight difference with a preset difference threshold, invalid nodes are identified and removed from the semantic interest tree. This allows the semantic interest tree to more accurately reflect the current interests of the target subject, improving the accuracy of the semantic interest tree. Using the semantic interest tree for subsequent video recommendations can recommend videos that better match the target subject's interests, thereby enhancing the user experience.

[0093] In an optional embodiment, the method further includes: determining a second level corresponding to the second semantic codeword in the semantic interest tree based on the position of the second semantic codeword in the second semantic code group; performing content matching on the semantic nodes at the second level in the semantic interest tree with the second semantic codeword; and in response to determining that the match fails, adding a semantic node corresponding to the second semantic codeword in the second level of the semantic interest tree.

[0094] The position of the second semantic codeword in the second semantic code group is used to describe the relative relationship between the semantic levels represented by the second semantic codeword. The relative relationship between the semantic levels is used to distinguish high-level semantics from low-level semantics. The number of layers in the semantic interest tree is determined based on the relative relationship between the semantic levels. If the semantic level is divided into high-level semantics, mid-level semantics, and low-level semantics, then the semantic interest tree has three layers, corresponding to the hierarchical semantics, mid-level semantics, and low-level semantics, respectively.

[0095] The second level refers to the semantic node corresponding to the second semantic codeword, and its level in the semantic interest tree. Content matching is performed on the semantic nodes and the second semantic codeword belonging to the same semantic level. A successful content match indicates that the semantics corresponding to the second semantic codeword are already in the semantic interest tree and do not need to be added again. A failed content match indicates that there is no semantics corresponding to the second semantic codeword in the semantic interest tree, and this semantics is a new point of interest for the target object. At this point, a new semantic node corresponding to the second semantic codeword is added to the corresponding level of the semantic interest tree.

[0096] The above technical solution provides a practical and feasible semantic interest tree update solution for updating semantic nodes in the semantic interest tree. When updating semantic nodes in the semantic interest tree, the level of the semantic node in the semantic interest tree and the content of the semantic node are comprehensively considered, thereby improving the accuracy of the semantic interest tree.

[0097] Figure 5 This is a flowchart of another video recommendation method provided according to an embodiment of the present disclosure; this embodiment is an optional solution proposed based on the above embodiment.

[0098] See also Figure 5 The video recommendation method provided in this embodiment includes:

[0099] S501: Obtain at least two candidate videos that match the target object.

[0100] S502: Determine a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video.

[0101] The first semantic code group includes at least two first semantic code words.

[0102] S503, obtaining a pre-built semantic interest tree for the target object; wherein the semantic interest tree is built according to input operations corresponding to historical videos and second semantic code groups corresponding to the historical videos, and the historical videos are videos that the target object has viewed.

[0103] The second semantic code group includes at least two second semantic code words. The first semantic code group and the second semantic code group have the same length. In other words, the number of the first semantic code words and the second semantic code words is the same.

[0104] S504: Determine a first level corresponding to the first semantic codeword in the semantic interest tree according to the position of the first semantic codeword in the first semantic code group.

[0105] The position of the first semantic codeword in the first semantic code group is used to describe the relative relationship between the semantic levels represented by the first semantic codeword. The first level refers to the level of the semantic node corresponding to the first semantic codeword in the semantic interest tree.

[0106] S505 : Perform content matching on the semantic nodes at the first level in the semantic interest tree and the first semantic codeword, so as to determine a target node matching the first semantic codeword from the semantic nodes at the first level.

[0107] Content matching is performed on semantic nodes and the first semantic codeword belonging to the same semantic level. A successful content matching indicates that the semantics corresponding to the first semantic codeword is of interest to the target object.

[0108] The target node refers to the semantic node in the first level of the semantic interest tree that matches the first semantic codeword.

[0109] S506 , determining a recommendation weight of each candidate video based on the level of the target node in the semantic interest tree and the weight values ​​of the positive component and the negative component in the interest weight.

[0110] The target node's level in the semantic interest tree can determine whether the semantics corresponding to the target node are high-level or low-level. Based on the target node's level in the semantic interest tree, the target node's level factor can be determined. Generally speaking, the target node's level factor is positively correlated with its level in the semantic interest tree; the deeper the target node's level, the larger its level factor.

[0111] Optionally, the hierarchical factor of the target node is used to weight the weight values ​​of the positive component and the negative component in the interest weight, and the recommendation weight of the candidate video is determined based on the weighted result. Optionally, the formula is used Determine the recommendation weight of the candidate video. Among them, pos_weight+=value(tree pos , rqvae_key)*layer_weight, neg_weight+=value(tree neg , rqvae_key)*layer_weight. Here, layer_weight represents the layer factor, value(tree pos , rqvae_key) and value(tree neg , rqvae_key) represent the weight values ​​of the positive component and negative component corresponding to the target node respectively.

[0112] S507: Determine the recommendation order based on the recommendation weight of each candidate video.

[0113] S508: Execute a video recommendation operation according to the recommendation order.

[0114] Optionally, the candidate videos are sorted in descending order of recommendation weights to determine a recommendation order among the candidate videos. Optionally, candidate videos with large recommendation weights are preferentially recommended to the target object.

[0115] This disclosed technical solution not only considers the interest weight associated with the input operation when determining the recommendation order among candidate videos, but also considers the target node's level in the semantic interest tree. Assigning different weights to semantic representations at different levels of abstraction helps improve the accuracy of the recommendation order, recommending videos that better match the target's interests and enhancing the user experience.

[0116] In an optional embodiment, the method further includes: determining the number of times the interest weight of the target node is adjusted within a second preset time period based on the weight adjustment time corresponding to the semantic node; in response to determining that the number of adjustments is less than or equal to the preset number, determining the target node as a new exploration node; using a preset new exploration factor, correcting the recommendation weight of the candidate video to obtain a correction result, and using the correction result to update the recommendation weight of the candidate video.

[0117] Among them, the weight adjustment time refers to the time for adjusting the interest weight of the semantic node. The weight adjustment time corresponding to the semantic node can be used to determine how many times the target object has browsed the semantics corresponding to the target node within the second preset time period. Alternatively, it can also be understood as how many times the video with the semantics corresponding to the target node is recommended to the target object within the second preset time period. Among them, the specific value of the second preset time period is determined according to actual business needs and is not limited here. Exemplarily, the second preset time period can be 14 days. Optionally, the second preset time period is used as an interest exploration cycle, and interest exploration is performed on the target object at intervals of the second preset time period.

[0118] The preset number of times is used to determine whether the target node is a new exploration node. A target node is considered a new exploration node only if the number of times is less than or equal to the preset number. The specific value of the preset number of times is determined based on actual business needs and is not limited here. For example, the preset number of times is 3.

[0119] The specific value of the new exploration factor is based on actual business needs and is not limited here. The new exploration factor is used to quantify the necessity of interest exploration of the target object for the target node. Exemplarily, the new exploration factor is set to 1.5. The preset new exploration factor is used to correct the recommendation weight of the candidate video, and the correction result is used to update the recommendation weight of the candidate video. Optionally, the preset new exploration factor is used to perform weighted processing on the recommendation weight of the candidate video, and the weighted processing result is updated as the recommendation weight of the candidate video.

[0120] The above technical solution considers the number of times videos with semantic meanings corresponding to the target node were recommended to the target user within the second preset period when determining the recommendation order among candidate videos. Based on this information, it determines whether interest exploration of the target user is necessary. This effectively explores user interests and recommends videos that better match their interests, thereby improving the user experience.

[0121] Figure 6 This is a schematic diagram of the structure of a video recommendation device provided according to an embodiment of the present disclosure. This embodiment of the present disclosure can be applied to recommending videos to users. The device can be implemented using software and / or hardware and can implement the video recommendation method described in any embodiment of the present disclosure.

[0122] like Figure 6 As shown, the video recommendation device 600 includes:

[0123] The candidate video acquisition module 601 is used to acquire at least two candidate videos that match the target object;

[0124] The semantic code group determination module 602 is configured to determine a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video;

[0125] An interest tree acquisition module 603 is configured to acquire a pre-built semantic interest tree for the target object; wherein the semantic interest tree is constructed based on input operations corresponding to historical videos and second semantic code groups corresponding to the historical videos, wherein the historical videos are videos that the target object has viewed;

[0126] A recommendation order determination module 604 is configured to determine a recommendation order among the candidate videos based on the first semantic code group and the semantic interest tree;

[0127] The video recommendation module 605 is configured to perform a video recommendation operation according to the recommendation order.

[0128] The disclosed technical solution pre-constructs a semantic interest tree for a target object, using it to describe the multi-level semantics of interest to the target object. A first semantic code group is used to describe at least two levels of semantics for candidate videos. Based on the semantic interest tree and the first semantic code group, a recommendation order is determined for each candidate video. This solution fully exploits the relationship between video semantics and user interests, enabling video recommendations based on multi-level semantics. This effectively improves the accuracy and personalization of video recommendations, thereby enhancing the user experience.

[0129] Optionally, the semantic node in the semantic interest tree corresponds to the second semantic codeword in the second semantic code group, and the level of the semantic node in the semantic interest tree corresponds to the position of the second semantic codeword in the second semantic code group; the interest weight associated with the semantic node is determined based on the input operation corresponding to the historical video.

[0130] Optionally, the recommendation order determination module 604 is specifically configured to determine the recommendation order based on the first semantic codeword in the first semantic code group, the semantic nodes in the semantic interest tree, and the interest weights associated with the semantic nodes.

[0131] Optionally, the device 600 also includes: a tendency and duration determination module, which is used to determine the interest tendency information and browsing duration information of the target object for the historical video based on the input operation in response to determining that the second semantic code group includes a second semantic code word corresponding to the semantic node; a target component determination module, which is used to determine the target component to be adjusted in the weight component of the interest weight based on the interest tendency information; a target step determination module, which is used to determine the target step used to adjust the target component based on the browsing duration information and the level of the semantic node corresponding to the second semantic code word in the semantic interest tree; and a weight adjustment module, which is used to reconfigure the weight of the semantic node corresponding to the second semantic code word based on the target step size and the target component.

[0132] Optionally, the device 600 also includes: a weight value determination module, used to determine the weight values ​​of the positive component and the negative component in the interest weight corresponding to the semantic node within a first preset time period based on the weight adjustment time corresponding to the semantic node; a weight difference determination module, used to determine the weight difference between the weight value of the positive component and the weight value of the negative component; an invalid node determination module, used to determine the semantic node as an invalid node in response to determining that the weight difference is greater than a preset difference threshold; and an invalid node removal module, used to remove the invalid node from the semantic interest tree.

[0133] Optionally, the recommendation order determination module 604 includes: a first level determination submodule, used to determine the first level corresponding to the first semantic codeword in the semantic interest tree according to the position of the first semantic codeword in the first semantic code group; a target node determination submodule, used to match the semantic nodes at the first level in the semantic interest tree with the first semantic codeword to determine the target node matching the first semantic codeword from the semantic nodes at the first level; a recommendation weight determination submodule, used to determine the recommendation weight of each of the candidate videos based on the level of the target node in the semantic interest tree and the weight values ​​of the positive component and the negative component in the interest weight; a recommendation order determination submodule, used to determine the recommendation order based on the recommendation weight of each of the candidate videos.

[0134] Optionally, the device 600 also includes: an adjustment number determination module, used to determine the number of adjustments of the interest weight of the target node within a second preset time period based on the weight adjustment time corresponding to the semantic node; a new exploration node determination module, used to determine the target node as a new exploration node in response to determining that the adjustment number is less than or equal to the preset number; a recommendation weight correction module, used to use a preset new exploration factor to correct the recommendation weight of the candidate video to obtain a correction result, and use the correction result to update the recommendation weight of the candidate video.

[0135] Optionally, the device 600 also includes: a second-level determination module, used to determine the second level corresponding to the second semantic codeword in the semantic interest tree based on the position of the second semantic codeword in the second semantic code group; a content matching module, used to perform content matching on the semantic node at the second level in the semantic interest tree with the second semantic codeword; and a semantic node adding module, used to add a semantic node corresponding to the second semantic codeword in the second level of the semantic interest tree in response to determining that the match fails.

[0136] Optionally, the semantic code group determination module 602 includes: a semantic fusion submodule for determining a semantic fusion vector of the candidate video based on the text description and video frame description of the candidate video; and a semantic matching submodule for calculating the semantic similarity between the semantic fusion vector and the candidate semantic vector in a preset semantic code table; wherein the preset semantic code table is trainable;

[0137] A target semantic vector determination submodule is used to determine a target semantic vector that is semantically similar to the semantic fusion vector in the candidate semantic vectors based on the semantic similarity; a semantic codeword determination submodule is used to identify the position of the target semantic vector in the preset semantic code table and determine it as the first semantic codeword in the first semantic code group; a residual determination submodule is used to calculate the residual between the semantic fusion vector and the target semantic vector, and use the residual as a new semantic fusion vector.

[0138] The video recommendation device provided in the embodiments of the present disclosure can execute the video recommendation method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of executing the video recommendation method.

[0139] In the technical solution disclosed herein, the collected input operations are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse the collection, storage, use, processing, transmission, provision and disclosure of the input operations involved, which are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0140] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0141] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0142] like Figure 7As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0143] Multiple components in the electronic device 700 are connected to the I / O interface 705, including an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0144] The computing unit 701 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the video recommendation method. For example, in some embodiments, the video recommendation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the video recommendation method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the video recommendation method by any other appropriate means (e.g., by means of firmware).

[0145] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0146] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable video recommendation device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0147] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0148] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0149] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0150] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0151] Artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

[0152] Cloud computing refers to a technology system that provides network access to elastically scalable shared pools of physical or virtual resources. These resources can include servers, operating systems, networks, software, applications, and storage devices, and can be deployed and managed on-demand in a self-service manner. Cloud computing technology provides efficient and powerful data processing capabilities for the application of technologies such as artificial intelligence and blockchain, as well as for model training.

[0153] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0154] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A video recommendation method, comprising: Obtain at least two candidate videos that match the target object; Determining a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video; Obtain a pre-constructed semantic interest tree for the target object; wherein the semantic interest tree is constructed based on input operations corresponding to historical videos and a second semantic code group corresponding to the historical videos, the historical videos being videos that the target object has viewed; semantic nodes in the semantic interest tree correspond to second semantic code words in the second semantic code group, and the level at which the semantic nodes are located in the semantic interest tree corresponds to the position of the second semantic code word in the second semantic code group; and interest weights associated with the semantic nodes are determined based on the input operations corresponding to the historical videos; determining a recommendation order among the candidate videos based on the first semantic codeword in the first semantic code group, the semantic nodes in the semantic interest tree, and the interest weights associated with the semantic nodes; Execute the video recommendation operation according to the recommendation order; Wherein, the determining of the recommendation order among the candidate videos based on the first semantic codeword in the first semantic code group, the semantic nodes in the semantic interest tree, and the interest weights associated with the semantic nodes includes: determining a first level corresponding to the first semantic codeword in the semantic interest tree according to the position of the first semantic codeword in the first semantic code group; performing content matching on the semantic nodes at the first level in the semantic interest tree with the first semantic codeword to determine a target node matching the first semantic codeword from the semantic nodes at the first level; determining the recommendation weight of each candidate video based on the level of the target node in the semantic interest tree and the weight values ​​of the positive component and the negative component in the interest weight; and determining the recommendation order based on the recommendation weight of each candidate video; Among them, the hierarchical factor of the target node is determined based on the level of the target node in the semantic interest tree; the deeper the level of the target node, the greater the hierarchical factor of the target node; the hierarchical factor of the target node is used to weight the weight values ​​of the positive component and the negative component in the interest weight, and the recommendation weight of the candidate video is determined based on the weighted result.

2. The method according to claim 1, further comprising: In response to determining that the second semantic code group includes a second semantic codeword corresponding to the semantic node, based on the input operation, determining the target object's interest tendency information and browsing duration information for the historical video; Based on the interest tendency information, determining a target component to be adjusted in the weight components of the interest weight; determining a target step size for adjusting the target component based on the browsing duration information and a level of the semantic node corresponding to the second semantic codeword in the semantic interest tree; Based on the target step size and the target component, a weight of the semantic node corresponding to the second semantic codeword is reconfigured.

3. The method according to claim 1, further comprising: Determining weight values ​​of a positive component and a negative component of the interest weight corresponding to the semantic node within a first preset time period based on a weight adjustment time corresponding to the semantic node; Determining a weight difference between the weight value of the positive component and the weight value of the negative component; In response to determining that the weight difference is greater than a preset difference threshold, determining the semantic node as a failed node; The invalid node is removed from the semantic interest tree.

4. The method according to claim 1, further comprising: Determining the number of times the interest weight of the target node is adjusted within a second preset time period based on the weight adjustment time corresponding to the semantic node; In response to determining that the number of adjustments is less than or equal to a preset number, determining the target node as a new exploration node; The recommendation weight of the candidate video is modified by using a preset new exploration factor to obtain a modified result, and the recommendation weight of the candidate video is updated using the modified result.

5. The method according to claim 1, further comprising: determining, in the semantic interest tree, a second level corresponding to the second semantic codeword based on a position of the second semantic codeword in the second semantic code group; Performing content matching on the semantic nodes at the second level in the semantic interest tree and the second semantic codeword; In response to determining that the match fails, a semantic node corresponding to the second semantic codeword is added to the second level of the semantic interest tree.

6. The method according to claim 1, wherein The determining the first semantic code group corresponding to the candidate video includes: Determining a semantic fusion vector of the candidate video based on the text description and the video frame description of the candidate video; Calculating the semantic similarity between the semantic fusion vector and candidate semantic vectors in a preset semantic code table; wherein the preset semantic code table is trainable; Based on the semantic similarity, determining a target semantic vector semantically similar to the semantic fusion vector from among the candidate semantic vectors; Identify the position of the target semantic vector in the preset semantic code table as the first semantic codeword in the first semantic code group; The residual between the semantic fusion vector and the target semantic vector is calculated, and the residual is used as a new semantic fusion vector.

7. A video recommendation device, comprising: A candidate video acquisition module is used to acquire at least two candidate videos that match the target object; A semantic code group determination module, configured to determine a first semantic code group corresponding to the candidate video; wherein the first semantic code group is used to describe at least two levels of semantics of the candidate video; An interest tree acquisition module is configured to acquire a semantic interest tree pre-constructed for the target object; wherein the semantic interest tree is constructed based on input operations corresponding to historical videos and a second semantic code group corresponding to the historical videos, the historical videos being videos viewed by the target object; semantic nodes in the semantic interest tree correspond to second semantic code words in the second semantic code group, and the level at which the semantic nodes are located in the semantic interest tree corresponds to the position of the second semantic code word in the second semantic code group; and interest weights associated with the semantic nodes are determined based on the input operations corresponding to the historical videos; A recommendation order determination module, specifically configured to determine a recommendation order among the candidate videos based on the first semantic codeword in the first semantic code group, the semantic nodes in the semantic interest tree, and the interest weights associated with the semantic nodes; A video recommendation module, configured to perform a video recommendation operation according to the recommendation order; Wherein, the recommendation order determination module includes: a first level determination submodule, used to determine the first level corresponding to the first semantic codeword in the semantic interest tree according to the position of the first semantic codeword in the first semantic code group; a target node determination submodule, used to match the semantic nodes at the first level in the semantic interest tree with the first semantic codeword to determine the target node matching the first semantic codeword from the semantic nodes at the first level; a recommendation weight determination submodule, used to determine the recommendation weight of each candidate video based on the level of the target node in the semantic interest tree and the weight values ​​of the positive component and the negative component in the interest weight; a recommendation order determination submodule, used to determine the recommendation order based on the recommendation weight of each candidate video; Among them, the hierarchical factor of the target node is determined based on the level of the target node in the semantic interest tree; the deeper the level of the target node, the greater the hierarchical factor of the target node; the hierarchical factor of the target node is used to weight the weight values ​​of the positive component and the negative component in the interest weight, and the recommendation weight of the candidate video is determined based on the weighted result.

8. The apparatus according to claim 7, further comprising: a tendency and duration determination module for determining, in response to determining that the second semantic code group includes a second semantic codeword corresponding to the semantic node, the interest tendency information and browsing duration information of the target object for the historical video based on the input operation; a target component determination module, configured to determine a target component to be adjusted in the weight components of the interest weight based on the interest tendency information; a target step size determination module, configured to determine a target step size for adjusting the target component based on the browsing duration information and the level of the semantic node corresponding to the second semantic codeword in the semantic interest tree; A weight adjustment module is used to reconfigure the weight of the semantic node corresponding to the second semantic codeword based on the target step size and the target component.

9. The apparatus according to claim 7, further comprising: A weight value determination module, configured to determine weight values ​​of a positive component and a negative component in the interest weight corresponding to the semantic node within a first preset time period based on a weight adjustment time corresponding to the semantic node; A weight difference determination module, configured to determine a weight difference between the weight value of the positive component and the weight value of the negative component; a failure node determination module, configured to determine the semantic node as a failure node in response to determining that the weight difference is greater than a preset difference threshold; The invalid node removal module is used to remove the invalid node from the semantic interest tree.

10. The apparatus according to claim 7, further comprising: An adjustment times determination module, configured to determine the number of times the interest weight of the target node is adjusted within a second preset time period based on the weight adjustment time corresponding to the semantic node; A new exploration node determination module, configured to determine the target node as a new exploration node in response to determining that the number of adjustments is less than or equal to a preset number; The recommendation weight correction module is used to use a preset new exploration factor to correct the recommendation weight of the candidate video to obtain a correction result, and use the correction result to update the recommendation weight of the candidate video.

11. The apparatus according to claim 7, further comprising: a second level determining module, configured to determine, in the semantic interest tree, a second level corresponding to the second semantic codeword based on a position of the second semantic codeword in the second semantic code group; a content matching module, configured to perform content matching between the semantic nodes at the second level in the semantic interest tree and the second semantic codeword; The semantic node adding module is configured to, in response to determining that the matching fails, add a semantic node corresponding to the second semantic codeword in the second level of the semantic interest tree.

12. The device according to claim 7, wherein The semantic code group determination module includes: A semantic fusion submodule, configured to determine a semantic fusion vector of the candidate video based on the text description and the video frame description of the candidate video; A semantic matching submodule, configured to calculate the semantic similarity between the semantic fusion vector and candidate semantic vectors in a preset semantic code table; wherein the preset semantic code table is trainable; a target semantic vector determination submodule, configured to determine, from the candidate semantic vectors, a target semantic vector that is semantically similar to the semantic fusion vector based on the semantic similarity; a semantic codeword determination submodule, configured to identify the position of the target semantic vector in the preset semantic code table as the first semantic codeword in the first semantic code group; The residual determination submodule is used to calculate the residual between the semantic fusion vector and the target semantic vector, and use the residual as a new semantic fusion vector.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the video recommendation method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the video recommendation method according to any one of claims 1 to 6. 15 . A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the video recommendation method according to claim 1 .

Images