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

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.

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.