Video recommendation method and device, computer device and storage medium

A server system, comprising at least one processor and memory, the at least one memory storing computer-readable instructions, wherein the at least one processor is configured to execute the computer-readable instructions to perform steps comprising:

• inputting a video to a first feature extraction network;
• performing video feature extraction on at least one consecutive video frame in the video with the first feature extraction network to generate a video feature of the video;
• inputting user data of a user to a second feature extraction network;
• performing user feature extraction on the user data with the second feature extraction network to generate a user feature of the user, the user data being discrete;
• inputting at least one text corresponding to the video to a third feature extraction network;
• performing text feature extraction on the at least one text with the third feature extraction network to generate a text feature of the video, the at least one text being discrete;
• performing first feature fusion based at least on the video feature and the user feature to obtain a first recommendation probability of recommending the video to the user by:
• performing video-user feature fusion on the video feature and the user feature to obtain a first associated feature between the video and the user;
• performing text-user feature fusion on the text feature and the user feature to obtain a second associated feature between the at least one text and the user;
• and
• performing dot multiplication on the first associated feature and the second associated feature to obtain the first recommendation probability of recommending the video to the user;
• and
• determining, according to the first recommendation probability, whether to recommend the video to the user.

A non-transitory computer-readable storage medium, storing computer-readable instructions, the computer-readable instructions being configured to, when executed by one or more processors, cause the one or more processors to perform steps comprising:

• inputting a video to a first feature extraction network;
• performing video feature extraction on at least one consecutive video frame in the video with the first feature extraction network to generate a video feature of the video;
• inputting user data of a user to a second feature extraction network;
• performing user feature extraction on the user data with the second feature extraction network to generate a user feature of the user, the user data being discrete;
• inputting at least one text corresponding to the video to a third feature extraction network;
• performing text feature extraction on the at least one text with the third feature extraction network to generate a text feature of the video, the at least one text being discrete;
• performing first feature fusion based at least on the video feature and the user feature to obtain a first recommendation probability of recommending the video to the user by:
• performing video-user feature fusion on the video feature and the user feature to obtain a first associated feature between the video and the user;
• performing text-user feature fusion on the text feature and the user feature to obtain a second associated feature between the at least one text and the user;
• and
• performing dot multiplication on the first associated feature and the second associated feature to obtain the first recommendation probability of recommending the video to the user;
• and
• determining, according to the first recommendation probability, whether to recommend the video to the user.