Item recommendation method and related device thereof

A model training method, wherein the method comprises:

• obtaining first information, wherein the first information comprises attribute information of a user and attribute information of an item;
• processing the first information by using a first to-be-trained model to obtain a first processing result, wherein the first processing result determines a probability of tapping the item by the user, the probability of tapping the item by the user is used to determine the item recommended to the user, the first to-be-trained model is configured to:
• perform a linear operation on the first information to obtain second information;
• perform a nonlinear operation on the second information to obtain third information;
• and obtain the first processing result based on the third information;
• obtaining a target loss based on the probability of tapping the item by the user and a real probability of tapping the item by the user, wherein the target loss indicates a difference between the probability of tapping the item by the user and the real probability of tapping the item by the user;
• and
• updating a parameter of the first to-be-trained model based on the target loss until a model training condition is met, to obtain a first model.

An item recommendation apparatus, wherein the apparatus comprises a memory and a processor, the memory stores code, the processor is configured to execute the code, and when the code is executed, the item recommendation apparatus performs the method that comprises:

• obtaining first information, wherein the first information comprises attribute information of a user and attribute information of an item;
• and
• processing the first information by using a first model to obtain a first processing result, wherein the first processing result determines the item recommended to the user, and the first model is configured to:
• perform a linear operation on the first information to obtain second information;
• perform a nonlinear operation on the second information to obtain third information;
• and obtain the first processing result based on the third information.