Content recommendation method and apparatus, device, medium, and program product

A content recommendation apparatus, comprising:

• processing circuitry configured to:
• obtain a first feature representation of a first user in a first function platform, wherein the first feature representation of the first user is extracted from attribute data of the first user;
• obtain a second feature representation of a second user in the first function platform, wherein the second feature representation of the second user is extracted from attribute data of the second user;
• determine a cluster center corresponding to the first user based on the first feature representation and the second feature representation;
• obtain an extra-domain feature representation of the first user in a second function platform, wherein the extra-domain feature representation of the first user is extracted from first historical interaction data of the first user in the second function platform;
• obtain a mapping relationship function corresponding to the cluster center, wherein the mapping relationship function indicates a feature representation relationship between the second function platform and the first function platform;
• determine an intra-domain feature representation of the first user, wherein the intra-domain feature representation is based on mapping the extra-domain feature representation with the mapping relationship function;
• determine a target feature representation of the first user based on the intra-domain feature representation and the first feature representation;
• determine, from a candidate content recommendation pool, target content that matches the target feature representation of the first user;
• and
• push the target content to the first user.

A non-transitory computer-readable storage medium, storing instructions which when executed by a processor cause the processor to perform:

• obtaining a first feature representation of a first user in a first function platform, wherein the first feature representation of the first user is extracted from attribute data of the first user;
• obtaining a second feature representation of a second user in the first function platform, wherein the second feature representation of the second user is extracted from attribute data of the second user;
• determining a cluster center corresponding to the first user based on the first feature representation and the second feature representation;
• obtaining an extra-domain feature representation of the first user in a second function platform, wherein the extra-domain feature representation of the first user is extracted from first historical interaction data of the first user in the second function platform;
• obtaining a mapping relationship function corresponding to the cluster center, wherein the mapping relationship function indicates a feature representation relationship between the second function platform and the first function platform;
• determining an intra-domain feature representation of the first user based on mapping the extra-domain feature representation with the mapping relationship function;
• determining a target feature representation of the first user based on the intra-domain feature representation and the first feature representation;
• determining, from a candidate content recommendation pool, target content that matches the target feature representation of the first user;
• and
• pushing the target content to the first user.