Item Recommendation Method and Related Device Thereof

A method comprising:

• inputting N pieces of first information into a to-be-trained model to obtain a predicted item recommendation result, wherein the to-be-trained model is configured to:
• obtain the N pieces of first information, wherein an ith piece of the first information indicates an ith first item and an ith behavior, wherein the ith behavior is a behavior of a user for the ith first item, wherein N behaviors of the user correspond to M categories, and wherein i=1, . . . , N, N≥M, and M>1;
• process the N pieces of first information based on a multi-head self-attention mechanism to obtain N pieces of second information;
• obtain the predicted item recommendation result based on the N pieces of second information;
• determine, based on the predicted item recommendation result, a target item from K second items, wherein K≥1;
• and recommend the target item to the user;
• obtaining a target loss based on the predicted item recommendation result and a real item recommendation result, wherein the target loss indicates a difference between the predicted item recommendation result and the real item recommendation result;
• and
• obtaining a target model by updating a parameter of the to-be-trained model based on the target loss until a model training condition is met.

An apparatus, comprising:

• a memory configured to store instructions;
• and
• one or more processors configured to execute the instructions to cause the apparatus to:
• obtain N pieces of first information, wherein an ith piece of the first information indicates an ith first item and an ith behavior, wherein the ith behavior is a behavior of a user for the ith first item, wherein N behaviors of the user correspond to M categories, and wherein i=1, . . . , N, N≥M, and M>1;
• process the N pieces of first information based on a multi-head self-attention mechanism, to obtain N pieces of second information;
• obtain an item recommendation result based on the N pieces of second information;
• determine, based on the item recommendation result, a target item from K second items, wherein K≥1;
• and
• recommend the target item to the user.