Abstract: A user feature generation method is performed at a server, the method 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.

Abstract: A user feature generation method is performed at a server, the method 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.