User feature generation method and apparatus, device, and computer-readable storage medium

1. A user feature generation method, performed by a server having a processor and memory storing a plurality of instructions to be executed by the processor, the method comprising:

acquiring n groups of timing correspondences between target videos and corresponding user accounts, each group of timing correspondences comprising user accounts that have viewed a respective target video, the user accounts being sorted according to their corresponding viewing timestamps, n being a positive integer;

obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix, the word-embedding matrix comprising a word vector corresponding to each user account;

training the word-embedding matrix by using a loss function, the loss function being used for defining a similarity relationship between the user accounts according to a degree of similarity between their respective watch histories; and

determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account.

2. The method according to claim 1 , wherein the training the word-embedding matrix by using a loss function comprises:

training the word-embedding matrix by using a noise-contrastive estimation (NCE) loss function.

3. The method according to claim 2 , wherein the determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account comprises:

determining the word vector corresponding to each user account in the word-embedding matrix as the user feature of the user account in a case that a function value of the NCE loss function is less than a first termination threshold.

4. The method according to claim 1 , wherein the obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix comprises:

obtaining the word-embedding matrix by mapping the n groups of timing correspondences in a word vector mapping manner.

5. The method according to claim 1 , wherein the acquiring n groups of timing correspondences between target videos and corresponding user accounts comprises:

acquiring video viewing history records of m user accounts, each video viewing history record comprising a video identifier of a target video and viewing timestamps of the target video, m being a positive integer, the target video being at least one of a watched video and a searched-for video of a user corresponding to one of the m user accounts; and

generating, for each target video in the video viewing history records after the user accounts that have viewed the target video are sorted according to their corresponding viewing timestamps, timing correspondences between the target video and the user accounts.

6. The method according to claim 1 , further comprising:

acquiring a video identifier of a target video in a history record, and generating a video feature according to the video identifier, the history record being at least one of a watch history record and a search history record of the target video by a user account; and

training a candidate generation model using the user feature and the video feature as sample features, the trained candidate generation model being used for outputting watch probabilities of videos in a video library according to the user feature.

7. The method according to claim 6 , wherein the training a candidate generation model using the user feature and the video feature as sample features comprises:

dividing the user features and the video features into h+k groups of data sets, the h+k groups of data sets comprising h groups of training sets and k groups of assessment sets, both h and k being positive integers; and

inputting the user features and the video features in the h groups of training sets as the sample features into the candidate generation model, to obtain the trained candidate generation model; and

the method further comprises:

evaluating a recall rate of the trained candidate generation model by using the k groups of assessment sets; and

determining the trained candidate generation model as a to-be-used candidate generation model in a case that the recall rate of the trained candidate generation model reaches a second termination threshold.

8. A server comprising a processor and memory, the memory storing a plurality of instructions that, when executed by the processor, cause the processor to perform a plurality of operations including:

acquiring n groups of timing correspondences between target videos and corresponding user accounts, each group of timing correspondences comprising user accounts that have viewed a respective target video, the user accounts being sorted according to their corresponding viewing timestamps, n being a positive integer;

obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix, the word-embedding matrix comprising a word vector corresponding to each user account;

training the word-embedding matrix by using a loss function, the loss function being used for defining a similarity relationship between the user accounts according to a degree of similarity between their respective watch histories; and

determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account.

9. The server according to claim 8 , wherein the training the word-embedding matrix by using a loss function comprises:

training the word-embedding matrix by using a noise-contrastive estimation (NCE) loss function.

10. The server according to claim 9 , wherein the determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account comprises:

determining the word vector corresponding to each user account in the word-embedding matrix as the user feature of the user account in a case that a function value of the NCE loss function is less than a first termination threshold.

11. The server according to claim 8 , wherein the obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix comprises:

obtaining the word-embedding matrix by mapping the n groups of timing correspondences in a word vector mapping manner.

12. The server according to claim 8 , wherein the acquiring n groups of timing correspondences between target videos and corresponding user accounts comprises:

acquiring video viewing history records of m user accounts, each video viewing history record comprising a video identifier of a target video and viewing timestamps of the target video, m being a positive integer, the target video being at least one of a watched video and a searched-for video of a user corresponding to one of the m user accounts; and

generating, for each target video in the video viewing history records after the user accounts that have viewed the target video are sorted according to their corresponding viewing timestamps, timing correspondences between the target video and the user accounts.

13. The server according to claim 8 , wherein the operations further comprise:

acquiring a video identifier of a target video in a history record, and generating a video feature according to the video identifier, the history record being at least one of a watch history record and a search history record of the target video by a user account; and

training a candidate generation model using the user feature and the video feature as sample features, the trained candidate generation model being used for outputting watch probabilities of videos in a video library according to the user feature.

14. The server according to claim 13 , wherein the training a candidate generation model using the user feature and the video feature as sample features comprises:

dividing the user features and the video features into h+k groups of data sets, the h+k groups of data sets comprising h groups of training sets and k groups of assessment sets, both h and k being positive integers; and

inputting the user features and the video features in the h groups of training sets as the sample features into the candidate generation model, to obtain the trained candidate generation model; and

the operations further comprise:

evaluating a recall rate of the trained candidate generation model by using the k groups of assessment sets; and

determining the trained candidate generation model as a to-be-used candidate generation model in a case that the recall rate of the trained candidate generation model reaches a second termination threshold.

15. A non-transitory computer-readable storage medium storing a plurality of instructions that, when executed by a processor of a server, cause the server to perform a plurality of operations including:

acquiring n groups of timing correspondences between target videos and corresponding user accounts, each group of timing correspondences comprising user accounts that have viewed a respective target video, the user accounts being sorted according to their corresponding viewing timestamps, n being a positive integer;

obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix, the word-embedding matrix comprising a word vector corresponding to each user account;

training the word-embedding matrix by using a loss function, the loss function being used for defining a similarity relationship between the user accounts according to a degree of similarity between their respective watch histories; and

determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account.

16. The non-transitory computer-readable storage medium according to claim 15 , wherein the training the word-embedding matrix by using a loss function comprises:

training the word-embedding matrix by using a noise-contrastive estimation (NCE) loss function.

17. The non-transitory computer-readable storage medium according to claim 16 , wherein the determining a word vector corresponding to each user account in the trained word-embedding matrix as a user feature of the user account comprises:

determining the word vector corresponding to each user account in the word-embedding matrix as the user feature of the user account in a case that a function value of the NCE loss function is less than a first termination threshold.

18. The non-transitory computer-readable storage medium according to claim 15 , wherein the obtaining a word-embedding matrix by mapping the n groups of timing correspondences into the word-embedding matrix comprises:

obtaining the word-embedding matrix by mapping the n groups of timing correspondences in a word vector mapping manner.

19. The non-transitory computer-readable storage medium according to claim 15 , wherein the acquiring n groups of timing correspondences between target videos and corresponding user accounts comprises:

acquiring video viewing history records of m user accounts, each video viewing history record comprising a video identifier of a target video and viewing timestamps of the target video, m being a positive integer, the target video being at least one of a watched video and a searched-for video of a user corresponding to one of the m user accounts; and

generating, for each target video in the video viewing history records after the user accounts that have viewed the target video are sorted according to their corresponding viewing timestamps, timing correspondences between the target video and the user accounts.

20. The non-transitory computer-readable storage medium according to claim 15 , wherein the operations further comprise:

acquiring a video identifier of a target video in a history record, and generating a video feature according to the video identifier, the history record being at least one of a watch history record and a search history record of the target video by a user account; and

training a candidate generation model using the user feature and the video feature as sample features, the trained candidate generation model being used for outputting watch probabilities of videos in a video library according to the user feature.