Heterogeneous graph neural network-based recommendation method

Abstract

The invention belongs to the technical field of recommendation systems, and relates to a heterogeneous graph neural network-based recommendation method, which comprises the following steps of: collecting a data set with social relationships among users, user-commodity interaction historical data and commodity category information, filtering invalid data and carrying out negative sampling; randomly selecting a user set and a related commodity set, and carrying out multi-order graph sampling and mapping; node feature extraction: inputting the constructed graph into a heterogeneous graph neural network for processing to obtain a fusion node embedding vector of the nodes, wherein for the commodity nodes which do not need to be subjected to the re-calibration step, the fusion node embedding vector of the commodity nodes is the commodity fusion embedding vector; re-calibration: re-calibrating the user fusion node embedding vector to obtain a user final expression embedding vector; and performing preference prediction by using the user final representation embedding vector and the commodity fusion embedding vector, and obtaining a recommendation sequence. The method solves the problems of data sparsity and data missing, and has the advantages of being accurate in recommendation and the like.

Description

technical field [0001] The invention belongs to the technical field of recommendation systems, and relates to a recommendation method based on a heterogeneous graph neural network. Background technique [0002] In today's era of information explosion, the information that users receive every day has exceeded the scope that individuals can handle, and a large amount of irrelevant redundant information seriously interferes with the selection of relevant and useful information that users need. A recommendation system is an application that recommends products to target users based on their historical behavior and personal preferences. It can provide users with personalized and more useful relevant information, thereby effectively alleviating the problem of information overload. [0003] In recent years, information data has exploded with the development of the Internet and smart mobile devices. Recommendation systems have been widely used in various network services such as e-c...

AI Technical Summary

Problems solved by technology

Although the graph neural network can achieve excellent results in the collaborative filtering method, it is still plagued by the data sparsity problem of the collaborative filtering method.

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