Method and apparatus for recommending content, device, and medium

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• the memory storing instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, causing the at least one processor to perform operations, the operations comprising:
• determining, in response to a session start request, a current session-level structural preference of a user using a pretrained session-level structural preference model based on historical behavior data of the user during a historical session and a feature of a structure of a to-be-recommended content, the structure being determined by classifying the to-be-recommended content based on any classifying method of a content tag system, a learning target of the session-level structural preference model comprising a preference degree of the user for any structure within a session, and the current session-level structural preference being used as a target structural preference of the user;
• and
• determining each recommendation result of the user based on the target structural preference, the recommendation result comprising at least two structures and a recommendation content corresponding to each structure,
• wherein a training process of the session-level structural preference model comprises:
• using a plurality of training samples as model inputs, wherein each training sample is user behavior data generated during a session, each training sample comprises N sub-samples, each sub-sample is the user behavior data of each structure displayed for the session, and N is a natural number;
• and
• using a percentage of a click rate of each structure involved in each pre-annotated training sample as a model output to train the session-level structural preference model.

A non-transitory computer readable storage medium storing computer instructions, the computer instructions, when executed by a computer, causing the computer to perform operations, the operations comprising:

• determining, in response to a session start request, a current session-level structural preference of a user using a pretrained session-level structural preference model based on historical behavior data of the user during a historical session and a feature of a structure of a to-be-recommended content, the structure being determined by classifying the to-be-recommended content based on any classifying method of a content tag system, a learning target of the session-level structural preference model comprising a preference degree of the user for any structure within a session, and the current session-level structural preference being used as a target structural preference of the user;
• and
• determining each recommendation result of the user based on the target structural preference, the recommendation result comprising at least two structures and a recommendation content corresponding to each structure,
• wherein a training process of the session-level structural preference model comprises:
• using a plurality of training samples as model inputs, wherein each training sample is user behavior data generated during a session, each training sample comprises N sub-samples, each sub-sample is the user behavior data of each structure displayed for the session, and N is a natural number;
• and
• using a percentage of a click rate of each structure involved in each pre-annotated training sample as a model output to train the session-level structural preference model.