Graph network cold start recommendation method

Abstract

The invention discloses a graph network cold start recommendation method, which comprises the following steps of: inputting pre-scored user-article data into a trained graph network to obtain a recommendation result, the training comprising the following steps: acquiring a sampling local graph of a node set or a local sub-graph to be trained; performing distance re-marking on the sampling local graph to obtain a re-marked label; distributing initial features to nodes of the sampling local graph; obtaining a prediction label and a prediction score of the initial feature; calculating a node classification error by using the prediction label and the remarking label, and calculating a score prediction error by using the prediction score and the real score of the article-user; and performing calculating by using the node classification error and the score prediction error to obtain an overall error, and updating parameters of the graph network by using the overall error. According to the method, through local graph sampling and double-task learning, inductive node reasoning and connection prediction capabilities are further realized on the basis of a deductive graph reasoning task, and the method has a feature representation capability for out-of-graph nodes.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a graph network cold start recommendation method. Background technique [0002] In recent years, with the rise of graph networks, a series of algorithms based on graph networks have emerged. Among them, due to the natural fit between the data format in the recommendation system and the graph network structure, applying the graph network to the recommendation field has become the primary goal of many researchers. Among them, GC-MC as a representative model has achieved excellent performance on the recommendation field verification data set. [0003] However, a significant problem of this kind of graph network recommendation algorithm is that it cannot effectively deal with the cold start and sparse labeling problems in the recommendation environment. The underlying reason is that these methods are essentially transductive algorithms, which can only learn features in an over...

AI Technical Summary

Problems solved by technology

From the recommendation system level, methods based on such algorithms cannot provide effective services to systems or platforms that are in the period of user growth, and require extensive model training to match real-time user capacity

[0004] In addition, since many existing methods need to model and store the entire interaction matrix, when the number of users and items increases to a certain scale, the requirements for storage space also increase exponentially, which brings great challenges to storage media and devices. no small challenge

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