Recommendation system based on graph convolution technology

Abstract

A recommendation system based on graph convolution technology comprises a preprocessing module, a heterogeneous graph generation module, a model training module and a recommendation result generationmodule, wherein, the preprocessing module cleans the interaction records of the user and the article and performs the data cleaning and the format standardization operation, and generates the interaction sequence for each user and outputs the interaction sequence to the heterogeneous graph generation module; The heterogeneous graph generation module constructs three heterogeneous graphs representing user preferences, dependencies among items and similarities among users according to user interaction sequence data, and outputs the generated graph structure data to the model training module. Themodel training module trains the graph convolution model based on graph structure data and generates vector representation for each user and object. The recommendation result generation module calculates the user's preference for all items according to the vector expression, and generates the final recommendation result. The invention solves the problem that the number of the neighbors of each node is not equal, and the information of the neighbors of the nodes in the heterogeneous graph is mined by the convolution operation, so that the recommendation effect is improved.

Description

technical field [0001] The present invention relates to a technology in the field of information processing, specifically a system that uses graph convolution technology to mine interaction data between users and items to implement personalized item recommendation for users. Background technique [0002] Recommended system. According to the type of data used, recommender systems can mainly be classified into content-based recommendation and collaborative filtering recommendation. The former is based on the personal information of the user and the content information of the item to model the recommendation system. Collaborative filtering technology is based on the historical records of user interaction with items to model users' preferences for items. In general, collaborative filtering techniques, including matrix factorization methods, achieve better recommendation results than content-based recommendation methods. Collaborative filtering technology uses a two-dimensiona...

AI Technical Summary

Problems solved by technology

However, there is no research based on interaction sequence data, which simultaneously includes three types of information: user's preference for items, dependencies between items and user's behavior similarity.

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