Personalized recommendation method and system based on meta-path network representation learning

Abstract

The invention belongs to the technical field of personalized recommendation, and particularly relates to a personalized recommendation method and system based on meta-path network representation learning, and the method comprises the steps: building a heterogeneous information network through employing the social relation of a user, the scoring relation of the user for commodities, and the type relation between the commodities; for the heterogeneous information network, extracting a user feature vector matrix and a commodity feature vector matrix through a preset meta path; inputting the user feature representation vector and the commodity feature representation vector as a score prediction model, and completing training learning of the prediction model by optimizing a connection matrix in a target function learning model; and performing commodity prediction scoring on an unknown target customer by using the trained score prediction model, and performing personalized recommendation according to a commodity prediction score. The matrix decomposition performance can be improved by fusing the heterogeneous data information under the condition that the score data is sparse, the personalized recommendation performance is optimized, and the invention has a good application prospect.

Description

Technical field [0001] The invention belongs to the technical field of personalized recommendation, and particularly relates to a personalized recommendation method and system based on meta-path network representation learning. Background technique [0002] In the era of big data, it has become an urgent need for people to obtain the content they are interested in from massive data information. Recommender systems are an important tool for information retrieval. It can help users quickly find content they are interested in from Internet application platforms. At the same time, it can guide Internet service providers to provide appropriate content to users, improve users' experience, and promote the growth of transaction volume and economic benefits. Therefore, recommendation systems can cope with the information overload problem in the field of big data. Collaborative filtering is a state-of-the-art technology for recommendation systems. This method discovers the user's ...

AI Technical Summary

Problems solved by technology

Second, matrix factorization methods are less interpretable

The user and product feature matrix is ​​optimized by the gradient descent algorithm, which only has mathematical meaning and cannot explain the specific meaning of the user and product feature matrix

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