Data processing method and apparatus, program product, computer device, and medium

A computer device, comprising a memory and a processor, the memory having a computer program stored therein, the computer program, when executed by the processor, causing the computer device to perform a data processing method including:

• obtaining a heterogeneous conversion graph, the heterogeneous conversion graph comprising N object nodes and M resource nodes, each object node representing an object, each resource node representing a resource, N and M being positive integers, wherein a connecting edge between an object node and a resource node represents a conversion behavior from an object corresponding to the object node to a resource corresponding to the resource node;
• obtaining a homogeneous object graph corresponding to each of the N objects, the homogeneous object graph comprising a plurality of object feature nodes, each object feature node being configured to represent an object feature of the corresponding object;
• obtaining a homogeneous resource graph corresponding to each of the M resources, the homogeneous resource graph comprising a plurality of resource feature nodes, each resource feature node being configured to represent a resource feature of the corresponding resource;
• and
• training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction network, the trained prediction network being configured to predict a conversion index of an object of interest for a resource of interest.

A non-transitory computer-readable storage medium, having a computer program stored therein, the computer program being adapted to be loaded by a processor of a computer device and causing the computer device to perform a data processing method including:

• obtaining a heterogeneous conversion graph, the heterogeneous conversion graph comprising N object nodes and M resource nodes, each object node representing an object, each resource node representing a resource, N and M being positive integers, wherein a connecting edge between an object node and a resource node represents a conversion behavior from an object corresponding to the object node to a resource corresponding to the resource node;
• obtaining a homogeneous object graph corresponding to each of the N objects, the homogeneous object graph comprising a plurality of object feature nodes, each object feature node being configured to represent an object feature of the corresponding object;
• obtaining a homogeneous resource graph corresponding to each of the M resources, the homogeneous resource graph comprising a plurality of resource feature nodes, each resource feature node being configured to represent a resource feature of the corresponding resource;
• and
• training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction network, the trained prediction network being configured to predict a conversion index of an object of interest for a resource of interest.