阅读说明：本技术 视频推荐方法及装置 (Video recommendation method and device ) 是由 滕飞 于 2018-07-11 设计创作，主要内容包括：本公开涉及视频推荐方法及装置。该方法包括：根据目标用户的用户行为数据,确定目标用户对应的初始用户特征向量,并确定目标用户对应的正样本视频和负样本视频；通过神经网络获取目标用户对应的第一优化用户特征向量、正样本视频对应的优化视频特征向量以及负样本视频对应的优化视频特征向量；根据正样本视频对应的优化视频特征向量与第一优化用户特征向量之间的相关性,以及负样本视频对应的优化视频特征向量与第一优化用户特征向量之间的相关性,优化神经网络的参数；基于优化后的神经网络,向目标用户进行视频推荐。本公开能够提高视频推荐的准确性。(The disclosure relates to a video recommendation method and device. The method comprises the following steps: determining an initial user characteristic vector corresponding to a target user according to user behavior data of the target user, and determining a positive sample video and a negative sample video corresponding to the target user; acquiring a first optimized user characteristic vector corresponding to a target user, an optimized video characteristic vector corresponding to a positive sample video and an optimized video characteristic vector corresponding to a negative sample video through a neural network; optimizing parameters of a neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector; and performing video recommendation to the target user based on the optimized neural network. The video recommendation method and device can improve accuracy of video recommendation.)

1. A method for video recommendation, comprising:

determining an initial user characteristic vector corresponding to a target user according to user behavior data of the target user;

determining a positive sample video and a negative sample video corresponding to the target user according to the user behavior data of the target user;

determining an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video;

respectively inputting the initial user feature vector corresponding to the target user, the initial video feature vector corresponding to the positive sample video and the initial video feature vector corresponding to the negative sample video into a neural network, and obtaining a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video and an optimized video feature vector corresponding to the negative sample video;

optimizing parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and based on the optimized neural network, recommending videos to the target user.

2. The method of claim 1, wherein determining an initial user feature vector corresponding to a target user according to user behavior data of the target user comprises:

determining effective click videos in the user behavior data of the target user;

determining an initial video feature vector corresponding to the effective click video;

and determining an initial user characteristic vector corresponding to the target user according to the initial video characteristic vector corresponding to the effective click video.

3. The method of claim 2, wherein determining valid click videos in the user behavior data of the target user comprises:

and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as an effective click video.

4. The method according to claim 2 or 3, wherein determining the initial user feature vector corresponding to the target user according to the initial video feature vector corresponding to the valid click video comprises:

and determining the average value of the initial video feature vectors corresponding to the effective click videos as the initial user feature vector corresponding to the target user.

5. The method according to claim 2 or 3, wherein determining the initial user feature vector corresponding to the target user according to the initial video feature vector corresponding to the valid click video comprises:

determining the weight corresponding to the effective click video according to the ratio of the watching time length corresponding to the effective click video to the video time length;

and calculating the weighted sum of the initial video feature vectors corresponding to the effective click videos according to the weights corresponding to the effective click videos to obtain the initial user feature vector corresponding to the target user.

6. The method of claim 1, wherein determining the positive sample video and the negative sample video corresponding to the target user according to the user behavior data of the target user comprises:

determining effective click videos in the user behavior data of the target user;

determining the effective click video as a positive sample video corresponding to the target user;

and determining a negative sample video corresponding to the target user according to videos, except the effective click video, in the videos displayed to the target user.

7. The method of claim 6, wherein determining the negative sample video corresponding to the target user according to the videos displayed to the target user other than the valid click video comprises:

and sampling videos except the effective click video in the videos displayed by the target user to obtain a negative sample video corresponding to the target user.

8. The method of claim 1, wherein determining an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video comprises:

determining an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video;

and determining an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video.

9. The method of claim 1, wherein optimizing the parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector comprises:

determining an objective optimization function corresponding to the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and optimizing the target optimization function by adopting a gradient descent method so as to optimize the parameters of the neural network.

10. The method of claim 1, wherein performing video recommendations to the target user based on the optimized neural network comprises:

determining an initial video feature vector corresponding to the candidate video;

respectively inputting the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video into the optimized neural network, and acquiring a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video;

calculating the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector;

and recommending the video to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

11. A video recommendation apparatus, comprising:

the first determining module is used for determining an initial user characteristic vector corresponding to a target user according to user behavior data of the target user;

the second determining module is used for determining the positive sample video and the negative sample video corresponding to the target user according to the user behavior data of the target user;

a third determining module, configured to determine an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video;

an obtaining module, configured to input an initial user feature vector corresponding to the target user, an initial video feature vector corresponding to the positive sample video, and an initial video feature vector corresponding to the negative sample video into a neural network, respectively, and obtain a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video, and an optimized video feature vector corresponding to the negative sample video;

the optimization module is used for optimizing the parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and the recommendation module is used for recommending videos to the target user based on the optimized neural network.

12. The apparatus of claim 11, wherein the first determining module comprises:

the first determining submodule is used for determining an effective click video in the user behavior data of the target user;

the second determining submodule is used for determining an initial video feature vector corresponding to the effective click video;

and the third determining submodule is used for determining the initial user characteristic vector corresponding to the target user according to the initial video characteristic vector corresponding to the effective click video.

13. The apparatus of claim 12, wherein the first determination submodule is configured to:

and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as an effective click video.

14. The apparatus of claim 12 or 13, wherein the third determination submodule is configured to:

and determining the average value of the initial video feature vectors corresponding to the effective click videos as the initial user feature vector corresponding to the target user.

15. The apparatus of claim 12 or 13, wherein the third determination submodule comprises:

the determining unit is used for determining the weight corresponding to the effective click video according to the ratio of the watching time length corresponding to the effective click video to the video time length;

and the calculating unit is used for calculating the weighted sum of the initial video characteristic vectors corresponding to the effective click videos according to the weights corresponding to the effective click videos to obtain the initial user characteristic vectors corresponding to the target user.

16. The apparatus of claim 11, wherein the second determining module comprises:

the fourth determining submodule is used for determining an effective click video in the user behavior data of the target user;

a fifth determining submodule, configured to determine the valid click video as a positive sample video corresponding to the target user;

and the sixth determining submodule is used for determining the negative sample video corresponding to the target user according to the videos, except the effective click video, in the videos displayed to the target user.

17. The apparatus of claim 16, wherein the sixth determination submodule is configured to:

and sampling videos except the effective click video in the videos displayed by the target user to obtain a negative sample video corresponding to the target user.

18. The apparatus of claim 11, wherein the third determining module comprises:

a seventh determining submodule, configured to determine an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video;

and the eighth determining submodule is used for determining an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video.

19. The apparatus of claim 11, wherein the optimization module comprises:

a ninth determining submodule, configured to determine, according to a correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and a correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector, a target optimization function corresponding to the neural network;

and the optimization submodule is used for optimizing the target optimization function by adopting a gradient descent method so as to optimize the parameters of the neural network.

20. The apparatus of claim 11, wherein the recommendation module comprises:

a tenth determining submodule, configured to determine an initial video feature vector corresponding to the candidate video;

an obtaining sub-module, configured to input the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video into the optimized neural network, respectively, and obtain a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video;

the calculation sub-module is used for calculating the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector;

and the recommending submodule is used for recommending the video to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

21. A video recommendation apparatus, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any one of claims 1 to 10.

22. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 10.

Technical Field

The present disclosure relates to the field of video technologies, and in particular, to a video recommendation method and apparatus.

Background

Because the information of the network video is uneven and the content is complicated, operators of video websites often select a recommendation algorithm based on collaborative filtering to recommend the video. In recent years, with the rapid development of the internet industry and multimedia technology, the amount of network video and the amount of video users are rapidly increasing. The huge amount of videos and users make the scoring matrix in collaborative filtering sparse, resulting in low accuracy of video recommendation.

Disclosure of Invention

In view of this, the present disclosure provides a video recommendation method and apparatus.

According to an aspect of the present disclosure, there is provided a video recommendation method including:

determining an initial user characteristic vector corresponding to a target user according to user behavior data of the target user;

determining a positive sample video and a negative sample video corresponding to the target user according to the user behavior data of the target user;

determining an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video;

respectively inputting the initial user feature vector corresponding to the target user, the initial video feature vector corresponding to the positive sample video and the initial video feature vector corresponding to the negative sample video into a neural network, and obtaining a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video and an optimized video feature vector corresponding to the negative sample video;

optimizing parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and based on the optimized neural network, recommending videos to the target user.

In a possible implementation manner, determining an initial user feature vector corresponding to a target user according to user behavior data of the target user includes:

determining effective click videos in the user behavior data of the target user;

determining an initial video feature vector corresponding to the effective click video;

and determining an initial user characteristic vector corresponding to the target user according to the initial video characteristic vector corresponding to the effective click video.

In one possible implementation, determining a valid click video in the user behavior data of the target user includes:

and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as an effective click video.

In a possible implementation manner, determining an initial user feature vector corresponding to the target user according to an initial video feature vector corresponding to the valid click video includes:

and determining the average value of the initial video feature vectors corresponding to the effective click videos as the initial user feature vector corresponding to the target user.

In a possible implementation manner, determining an initial user feature vector corresponding to the target user according to an initial video feature vector corresponding to the valid click video includes:

determining the weight corresponding to the effective click video according to the ratio of the watching time length corresponding to the effective click video to the video time length;

and calculating the weighted sum of the initial video feature vectors corresponding to the effective click videos according to the weights corresponding to the effective click videos to obtain the initial user feature vector corresponding to the target user.

In a possible implementation manner, determining a positive sample video and a negative sample video corresponding to the target user according to the user behavior data of the target user includes:

determining effective click videos in the user behavior data of the target user;

determining the effective click video as a positive sample video corresponding to the target user;

and determining a negative sample video corresponding to the target user according to videos, except the effective click video, in the videos displayed to the target user.

In a possible implementation manner, determining a negative sample video corresponding to the target user according to a video other than the valid click video in the video displayed to the target user includes:

and sampling videos except the effective click video in the videos displayed by the target user to obtain a negative sample video corresponding to the target user.

In one possible implementation, determining an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video includes:

determining an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video;

and determining an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video.

In one possible implementation, optimizing the parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector includes:

determining an objective optimization function corresponding to the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and optimizing the target optimization function by adopting a gradient descent method so as to optimize the parameters of the neural network.

In one possible implementation, performing video recommendation to the target user based on the optimized neural network includes:

determining an initial video feature vector corresponding to the candidate video;

respectively inputting the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video into the optimized neural network, and acquiring a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video;

calculating the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector;

and recommending the video to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

According to another aspect of the present disclosure, there is provided a video recommendation apparatus including:

the first determining module is used for determining an initial user characteristic vector corresponding to a target user according to user behavior data of the target user;

the second determining module is used for determining the positive sample video and the negative sample video corresponding to the target user according to the user behavior data of the target user;

a third determining module, configured to determine an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video;

an obtaining module, configured to input an initial user feature vector corresponding to the target user, an initial video feature vector corresponding to the positive sample video, and an initial video feature vector corresponding to the negative sample video into a neural network, respectively, and obtain a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video, and an optimized video feature vector corresponding to the negative sample video;

the optimization module is used for optimizing the parameters of the neural network according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector;

and the recommendation module is used for recommending videos to the target user based on the optimized neural network.

In one possible implementation manner, the first determining module includes:

the first determining submodule is used for determining an effective click video in the user behavior data of the target user;

the second determining submodule is used for determining an initial video feature vector corresponding to the effective click video;

and the third determining submodule is used for determining the initial user characteristic vector corresponding to the target user according to the initial video characteristic vector corresponding to the effective click video.

In one possible implementation, the first determining sub-module is configured to:

and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as an effective click video.

In one possible implementation, the third determining sub-module is configured to:

and determining the average value of the initial video feature vectors corresponding to the effective click videos as the initial user feature vector corresponding to the target user.

In one possible implementation, the third determining sub-module includes:

the determining unit is used for determining the weight corresponding to the effective click video according to the ratio of the watching time length corresponding to the effective click video to the video time length;

and the calculating unit is used for calculating the weighted sum of the initial video characteristic vectors corresponding to the effective click videos according to the weights corresponding to the effective click videos to obtain the initial user characteristic vectors corresponding to the target user.

In one possible implementation manner, the second determining module includes:

the fourth determining submodule is used for determining an effective click video in the user behavior data of the target user;

a fifth determining submodule, configured to determine the valid click video as a positive sample video corresponding to the target user;

and the sixth determining submodule is used for determining the negative sample video corresponding to the target user according to the videos, except the effective click video, in the videos displayed to the target user.

In one possible implementation, the sixth determining sub-module is configured to:

and sampling videos except the effective click video in the videos displayed by the target user to obtain a negative sample video corresponding to the target user.

In one possible implementation manner, the third determining module includes:

a seventh determining submodule, configured to determine an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video;

and the eighth determining submodule is used for determining an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video.

In one possible implementation, the optimization module includes:

a ninth determining submodule, configured to determine, according to a correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and a correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector, a target optimization function corresponding to the neural network;

and the optimization submodule is used for optimizing the target optimization function by adopting a gradient descent method so as to optimize the parameters of the neural network.

In one possible implementation, the recommendation module includes:

a tenth determining submodule, configured to determine an initial video feature vector corresponding to the candidate video;

an obtaining sub-module, configured to input the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video into the optimized neural network, respectively, and obtain a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video;

the calculation sub-module is used for calculating the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector;

and the recommending submodule is used for recommending the video to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

According to another aspect of the present disclosure, there is provided a video recommendation apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the above method.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

The video recommendation method and device in each aspect of the disclosure train the neural network based on the positive sample video and the negative sample video corresponding to the target user, mine the deep semantic relationship between the target user and the video, extract the low-level feature information of the target user and the video by using the feature extraction capability of deep learning of the neural network, generate more abstract high-level feature information, and perform video recommendation on the basis, so that the accuracy of video recommendation can be improved.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flow diagram of a video recommendation method according to an embodiment of the present disclosure.

Fig. 2 shows an exemplary flowchart of the video recommendation method step S11 according to an embodiment of the present disclosure.

Fig. 3 shows an exemplary flowchart of the video recommendation method step S113 according to an embodiment of the disclosure.

Fig. 4 shows an exemplary flowchart of the video recommendation method step S12 according to an embodiment of the present disclosure.

Fig. 5 shows an exemplary flowchart of the video recommendation method step S15 according to an embodiment of the present disclosure.

Fig. 6 shows a schematic diagram of a neural network in a video recommendation method according to an embodiment of the present disclosure.

Fig. 7 shows an exemplary flowchart of the video recommendation method step S16 according to an embodiment of the present disclosure.

Fig. 8 shows a block diagram of a video recommendation device according to an embodiment of the present disclosure.

Fig. 9 shows an exemplary block diagram of a video recommendation device according to an embodiment of the present disclosure.

Fig. 10 is a block diagram illustrating an apparatus 1900 for video recommendation, according to an example embodiment.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Fig. 1 shows a flow diagram of a video recommendation method according to an embodiment of the present disclosure. As shown in fig. 1, the method includes steps S11 through S16.

In step S11, an initial user feature vector corresponding to the target user is determined according to the user behavior data of the target user.

In a possible implementation manner, the initial user feature vector corresponding to the target user may be determined according to the user behavior data of the target user within the specified time period. For example, the specified time period is 1 month.

In a possible implementation manner, the initial user feature vector corresponding to the target user may be determined according to click data in the user behavior data of the target user.

In step S12, according to the user behavior data of the target user, a positive sample video and a negative sample video corresponding to the target user are determined.

In a possible implementation manner, the positive sample video and the negative sample video corresponding to the target user may be determined according to the user behavior data of the target user within the specified time period.

In a possible implementation manner, the positive sample video and the negative sample video corresponding to the target user may be determined according to the effective click video in the user behavior data of the target user.

In another possible implementation manner, the positive sample video and the negative sample video corresponding to the target user may be determined according to the click video in the user behavior data of the target user. In this implementation manner, a video clicked by a target user in the user behavior data of the target user may be determined as a positive sample video corresponding to the target user, and a video not clicked by the target user in the videos displayed to the target user may be determined as a negative sample video corresponding to the target user.

In step S13, an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video are determined.

In one possible implementation, determining an initial video feature vector corresponding to a positive sample video and an initial video feature vector corresponding to a negative sample video includes: determining an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video; and determining an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video. In the embodiment of the disclosure, the initial video feature vector corresponding to the video may be determined according to the text description information of the video. For example, the text description information of the video may include one or more of a title of the video, a content introduction of the video, a production region of the video, a director of the video, an editor of the video, a tag of the video, an upload time of the video, an uploader of the video, and the like.

In one possible implementation manner, determining an initial video feature vector corresponding to a video according to text description information of the video may include: segmenting words of the text description information of the video to obtain a word segmentation result; determining keywords of the video according to TF-IDF (Term Frequency-Inverse Document Term Frequency) of each Term in the segmentation result; and determining an initial video feature vector corresponding to the video according to the keywords of the video.

As an example of this implementation, the ICTCLAS word segmentation system may be used to segment the text description information of the video, and meanwhile, stop words in the text description information may be removed.

As an example of this implementation, the TF of a word in the word segmentation result may be equal to the frequency of occurrence of the word in the text description information of the video; the IDF of the word may be equal to the frequency with which the word appears in the text description information of all videos.

As an example of this implementation, words in the word segmentation result of the video whose TF-IDF is greater than the first threshold may be determined as keywords of the video.

As another example of this implementation, Q words with the largest TF-IDF in the word segmentation results of the video may be determined as the keywords of the video, where Q is a positive integer.

As an example of this implementation, the keywords of each video may be counted, a keyword library of the video may be constructed, and the keywords may be countedAnd total number of words, and establishing a keyword index. For example, if the total number of keywords in the keyword library is K, the initial video feature vector corresponding to the video may be represented as V_I＝{k₁,k₂,k₃,…,k_KIn which k is₁The TF-IDF value, k, representing the 1 st keyword in the keyword library for the video₂The TF-IDF value of the video on the 2 nd keyword in the keyword library, and so on.

In step S14, the initial user feature vector corresponding to the target user, the initial video feature vector corresponding to the positive sample video, and the initial video feature vector corresponding to the negative sample video are respectively input to the neural network, and a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video, and an optimized video feature vector corresponding to the negative sample video are obtained.

In the embodiment of the present disclosure, the neural network may be configured to abstract the initial user feature vector and the initial video feature vector layer by layer, and finally output the low-dimensional semantic-related first optimized user feature vector and optimized video feature vector.

In step S15, the parameters of the neural network are optimized according to the correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and the correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector.

For example, optimizing video feature vector y_vAnd the first optimized user feature vector y_UThe correlation between them can be expressed as

In step S16, a video recommendation is made to the target user based on the optimized neural network.

The method and the device for recommending the video train the target user train the neural network based on the positive sample video and the negative sample video corresponding to the target user, excavate the deep semantic relation between the target user and the video, extract the low-level characteristic information of the target user and the video by utilizing the deep learning characteristic extraction capability of the neural network, generate more abstract high-level characteristic information, and recommend the video based on the high-level characteristic information, so that the accuracy of video recommendation can be improved.

Fig. 2 shows an exemplary flowchart of the video recommendation method step S11 according to an embodiment of the present disclosure. As shown in fig. 2, step S11 may include step S111 to step S113.

In step S111, a valid click video in the user behavior data of the target user is determined.

In one possible implementation, determining a valid click video in the user behavior data of the target user includes: and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as the effective click video. For example, the viewing time period for the target user to view the video C is T_UCVideo C has video duration T_CIf T is_UCAnd T_CIf the ratio of (d) is greater than the validity threshold, it may be determined that the video C is a valid click video. For example, the validity threshold is 0.3. In some cases, since the user clicks the video only roughly to browse the video content, and the user does not really have an interest in the video, in order to enable the initial user feature vector to represent the interest of the target user better, the embodiment of the present disclosure determines the initial user feature vector corresponding to the target user only according to the effective click video in the user behavior data of the target user.

In step S112, an initial video feature vector corresponding to the valid click video is determined.

In the embodiment of the disclosure, the initial video feature vector corresponding to the effective click video may be determined according to the text description information of the effective click video.

In step S113, an initial user feature vector corresponding to the target user is determined according to the initial video feature vector corresponding to the valid click video.

In a possible implementation manner, determining an initial user feature vector corresponding to a target user according to an initial video feature vector corresponding to an effective click video includes: will effectively click the first point corresponding to the videoAnd determining the average value of the feature vectors of the starting video as the feature vector of the initial user corresponding to the target user. For example, the average value of the initial video feature vectors corresponding to the valid click videos in the specified time period may be determined as the initial user feature vector corresponding to the target user. For example, if the specified time period is 1 month, and the number of valid clicked videos in the specified time period is L, the initial user feature vector corresponding to the target user may be represented as

Wherein, V_IlAnd representing the initial video feature vector corresponding to the ith valid click video in the specified time period.

Fig. 3 shows an exemplary flowchart of the video recommendation method step S113 according to an embodiment of the disclosure. As shown in fig. 3, step S113 may include step S1131 and step S1132.

In step S1131, a weight corresponding to the effective click video is determined according to a ratio of the watching duration corresponding to the effective click video to the video duration.

For example, the corresponding weight of the effective click video can be expressed as

Wherein alpha is_lRepresents the ratio of the watching time length corresponding to the ith effective click video to the video time length, beta_lAnd L represents the number of effective click videos.

In step S1132, according to the weight corresponding to each effective click video, a weighted sum of the initial video feature vectors corresponding to each effective click video is calculated to obtain an initial user feature vector corresponding to the target user.

For example,

fig. 4 shows an exemplary flowchart of the video recommendation method step S12 according to an embodiment of the present disclosure. As shown in fig. 4, step S12 may include steps S121 to S123.

In step S121, a valid click video in the user behavior data of the target user is determined.

In this step, the effective click video in the user behavior data of the target user may be determined in the same manner as described above, and details are not repeated here.

In step S122, the valid click video is determined as the positive sample video corresponding to the target user.

In step S123, a negative sample video corresponding to the target user is determined according to a video other than the effective click video in the videos displayed to the target user.

In a possible implementation manner, determining a negative sample video corresponding to a target user according to a video other than an effective click video in videos displayed to the target user includes: and sampling videos except the effective click video in the videos displayed to the target user to obtain a negative sample video corresponding to the target user.

Fig. 5 shows an exemplary flowchart of the video recommendation method step S15 according to an embodiment of the present disclosure. As shown in fig. 5, step S15 may include step S151 and step S152.

In step S151, an objective optimization function corresponding to the neural network is determined according to a correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and a correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector.

In step S152, the objective optimization function is optimized by using a gradient descent method to optimize parameters of the neural network.

In one possible implementation, the probability that the target user U clicks to view a certain video V may be represented as P (V | U):

wherein γ represents a smoothing factor, v_ARepresenting a set of video collections. Theoretically, set v_AShould beIncluding all positive and negative examples. However, in actual operation, the negative sample is very large, and in order to save calculation cost and improve training efficiency of the neural network, the negative sample can be sampled in a certain manner. For example, a positive sample combination (U, V) may be taken⁺) And three negative examples (U, V)^-) Combined as a video set v_A. During the experiment, the combination of the samples is found to have little influence on the final result. Thus, the above formula can be described approximately as:

wherein

We can train parameters in the neural network with a maximum likelihood function, i.e. equivalent to performing a minimization calculation on a negative log-likelihood function. The negative log-likelihood function equation is as follows:

l (θ) is the objective optimization function for the neural network. Where θ represents the set of parameters W in the neural network_i，b_i}。

Because the parameters are derivable, embodiments of the present disclosure may employ a gradient descent method to optimize the objective optimization function,wherein λ is_tIs the learning rate, θ, of the t-th iteration_tAnd theta_t-1The parameters of the neural network at the t-th iteration and the t-1 st iteration are respectively.

In another possible implementation, the objective optimization function may be expressed as L (θ) ═ Σ_Vg (U, V), wherein g (U, V) ═ -B · log σ (U, V) - (1-B) · log σ (U, V). For example, for a positive sample, B ═ 1; for negative samples, B is 0.

Fig. 6 shows a schematic diagram of a neural network in a video recommendation method according to an embodiment of the present disclosure. In the example shown in fig. 6, the network model of the neural network is composed of two parts, one part being a user model and the other part being a video model. The input to the neural network may be a high-dimensional feature vector U_IAnd V_IThe output may be a low-dimensional dense semantic feature vector U_OAnd V_OThe method can be regarded as abstract description of the neural network after feature extraction of low-level text features. For example, a neural network includes N-1 hidden layers (only 3 shown in FIG. 6), x represents an input vector, y represents an output vector, h_iDenotes an implicit layer in a neural network, i-1, 2,3_iWeight matrix representing the i-th layer in the network, b_iIndicating the bias of the ith layer, then:

h₁＝W₁x+b₁，

h_i＝f(W_ih_i-1+b_i),i＝1,2,3,...,N-1，

y＝f(W_Nh_N-1+b_N)。

where f (x) represents the activation function, which in the disclosed embodiment is the activation function of the hidden layer and the output layer. For example,

for the target user, x_U＝U_I，y_U＝U_O(ii) a For video, x_V＝V_I，y_V＝V_O。

Fig. 7 shows an exemplary flowchart of the video recommendation method step S16 according to an embodiment of the present disclosure. As shown in fig. 7, step S16 may include steps S161 to S164.

In step S161, an initial video feature vector corresponding to the candidate video is determined.

In one possible implementation, before determining the initial video feature vector corresponding to the candidate video, the method further includes: and carrying out filtering processing and fusion processing on the videos in the video library to obtain candidate videos.

As an example of this implementation, filtering the videos in the video library may include: the number of the missing fields of the text description information in the filtering video library is more than or equal to M₁Wherein M is₁Is a positive integer. By filtering the missing field number of the text description information in the video library, which is more than or equal to M₁The video recommendation method can ensure the data integrity and reliability of the candidate video in the video recommendation process, thereby further ensuring the accuracy of video recommendation. For example, the textual description information includes 9 fields: a title field, a content introduction field, a production area field, a director field, a drama field, a tag field, an upload time field, and an upload person field. If M is₁Equal to 3, the text description information of a certain video includes 5 fields, i.e. the number of missing fields of the text description information of the video is 4, then the video can be filtered.

As an example of this implementation, performing the fusion process on the videos in the video library may include: if the number of the same field of the text description information of a plurality of videos in the video library is more than or equal to M₂One of the plurality of videos is reserved as a candidate video, so that the video recommendation efficiency can be improved, wherein M is₂Is a positive integer. For example, M₂Equal to 7, if 8 fields in the text description information of two videos are the same, one of the two videos can be reserved as a candidate video. For example, a video with a sharper image quality of the two videos may be retained as the candidate video.

In step S162, the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video are respectively input to the optimized neural network, so as to obtain a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video.

In the embodiment of the present disclosure, the neural network may be configured to abstract the initial user feature vector and the initial video feature vector layer by layer, and finally output a second optimized user feature vector and an optimized video feature vector that are semantically related in a low-dimensional manner.

In step S163, a correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector is calculated.

The calculation method of the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector is similar to the above, and is not repeated here.

In step S164, video recommendation is performed to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

In one possible implementation manner, candidate videos corresponding to Z optimized video feature vectors with the maximum correlation with a second optimized user feature vector may be recommended to a target user, where Z is a positive integer.

The method for recommending the video based on the content analyzes and processes the text content information of the video by utilizing the technology of deep learning in the aspect of natural language processing, extracts the feature vector of the semantic layer, constructs a neural network model, deeply excavates the internal relation between the video and the video, and further recommends the video, thereby providing the method for recommending the video based on the content, and solving the cold start problem and the data sparse problem in the collaborative filtering recommendation algorithm. For example, for a user who likes a current news video, real-time reports in China and the United states are all interested, so that the keywords "China" and "United states" have a certain correlation, and are not relatively independent, so that the video with the keyword "China" and the video with the keyword "United states" have a certain correlation. According to the video recommendation method and device, the internal relation between the videos is deeply mined through the neural network, and the accuracy of video recommendation can be improved.

Fig. 8 shows a block diagram of a video recommendation device according to an embodiment of the present disclosure. As shown in fig. 8, the apparatus includes: the first determining module 21 is configured to determine an initial user feature vector corresponding to a target user according to user behavior data of the target user; the second determining module 22 is configured to determine, according to the user behavior data of the target user, a positive sample video and a negative sample video corresponding to the target user; a third determining module 23, configured to determine an initial video feature vector corresponding to the positive sample video and an initial video feature vector corresponding to the negative sample video; the obtaining module 24 is configured to input the initial user feature vector corresponding to the target user, the initial video feature vector corresponding to the positive sample video, and the initial video feature vector corresponding to the negative sample video into the neural network, and obtain a first optimized user feature vector corresponding to the target user, an optimized video feature vector corresponding to the positive sample video, and an optimized video feature vector corresponding to the negative sample video; the optimization module 25 is configured to optimize parameters of the neural network according to a correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and a correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector; and the recommending module 26 is used for recommending videos to the target user based on the optimized neural network.

Fig. 9 shows an exemplary block diagram of a video recommendation device according to an embodiment of the present disclosure. As shown in fig. 9:

in one possible implementation, the first determining module 21 includes: the first determining submodule 211 is configured to determine an effective click video in the user behavior data of the target user; a second determining submodule 212, configured to determine an initial video feature vector corresponding to the valid click video; and a third determining submodule 213, configured to determine an initial user feature vector corresponding to the target user according to the initial video feature vector corresponding to the valid click video.

In one possible implementation, the first determining submodule 211 is configured to: and determining the video with the ratio of the watching time length to the video time length larger than the effectiveness threshold value in the user behavior data of the target user as the effective click video.

In one possible implementation, the third determining submodule 213 is configured to: and determining the average value of the initial video feature vectors corresponding to the effective click videos as the initial user feature vector corresponding to the target user.

In one possible implementation, the third determining submodule 213 includes: the determining unit is used for determining the weight corresponding to the effective click video according to the ratio of the watching time length corresponding to the effective click video to the video time length; and the calculating unit is used for calculating the weighted sum of the initial video characteristic vectors corresponding to the effective click videos according to the weights corresponding to the effective click videos to obtain the initial user characteristic vectors corresponding to the target user.

In one possible implementation, the second determining module 22 includes: the fourth determining submodule 221 is configured to determine an effective click video in the user behavior data of the target user; a fifth determining sub-module 222, configured to determine the valid clicked video as a positive sample video corresponding to the target user; and a sixth determining submodule 223, configured to determine, according to videos other than the effective click video in the videos displayed to the target user, a negative sample video corresponding to the target user.

In one possible implementation, the sixth determining submodule 223 is configured to: and sampling videos except the effective click video in the videos displayed to the target user to obtain a negative sample video corresponding to the target user.

In one possible implementation, the third determining module 23 includes: a seventh determining submodule 231, configured to determine an initial video feature vector corresponding to the positive sample video according to the text description information of the positive sample video; the eighth determining submodule 232 is configured to determine an initial video feature vector corresponding to the negative sample video according to the text description information of the negative sample video.

In one possible implementation, the optimization module 25 includes: a ninth determining submodule 251, configured to determine, according to a correlation between the optimized video feature vector corresponding to the positive sample video and the first optimized user feature vector, and a correlation between the optimized video feature vector corresponding to the negative sample video and the first optimized user feature vector, an objective optimization function corresponding to the neural network; and an optimization submodule 252, configured to optimize the objective optimization function by using a gradient descent method to optimize parameters of the neural network.

In one possible implementation, the recommendation module 26 includes: a tenth determining submodule 261, configured to determine an initial video feature vector corresponding to the candidate video; the obtaining submodule 262 is configured to input the initial user feature vector corresponding to the target user and the initial video feature vector corresponding to the candidate video into the optimized neural network, respectively, and obtain a second optimized user feature vector corresponding to the target user and an optimized video feature vector corresponding to the candidate video; the calculating submodule 263 is configured to calculate a correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector; and the recommending submodule 264 is used for recommending the video to the target user according to the correlation between the optimized video feature vector corresponding to the candidate video and the second optimized user feature vector.

The method and the device for recommending the video train the target user train the neural network based on the positive sample video and the negative sample video corresponding to the target user, excavate the deep semantic relation between the target user and the video, extract the low-level characteristic information of the target user and the video by utilizing the deep learning characteristic extraction capability of the neural network, generate more abstract high-level characteristic information, and recommend the video based on the high-level characteristic information, so that the accuracy of video recommendation can be improved.

Fig. 10 is a block diagram illustrating an apparatus 1900 for video recommendation, according to an example embodiment. For example, the apparatus 1900 may be provided as a server. Referring to FIG. 10, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, MacOS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.