Meta-learning-based heterogeneous information network cold start recommendation method and device

Abstract

The embodiment of the invention provides a meta-learning-based heterogeneous information network cold start recommendation method and device. The method comprises the steps: obtaining first node information and second node information, and obtaining a first feature vector corresponding to a first node and a first feature vector corresponding to each second node; respectively inputting the first feature vector corresponding to the first node and the first feature vector corresponding to each second node into a pre-trained meta-learning model, and aggregating context semantics to obtain a secondfeature vector corresponding to the first node and a second feature vector corresponding to each second node; calculating the similarity between the second feature vector corresponding to the first node and the second feature vector corresponding to each second node; determining a target second node recommended to the first node based on each similarity. According to the method disclosed in the embodiment of the invention, the accuracy of the cold start recommendation result of the heterogeneous information network can be improved.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a cold-start recommendation method and device for a heterogeneous information network based on meta-learning. Background technique [0002] With the development of science and technology, recommendation systems have been widely developed and deployed in various online services, such as e-commerce platforms and news platforms. Recommendation systems can solve the problem of information overload for users. As the core of the recommendation system, the collaborative filtering of information aims to estimate the possibility of interaction between node information based on the interaction history, for example, to estimate the possibility of the user buying a product based on the user's past purchases and clicks and other historical operation records sex. But in reality, the interaction data of new users or new products is often very sparse, leading to the emergence of...

AI Technical Summary

Problems solved by technology

[0006] However, in practical applications, considering the security of information, it is difficult to obtain additional information of different nodes, and the accuracy of the obtained additional information cannot be guaranteed, making the cold start recommendation results of heterogeneous information networks inaccurate

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