Collaborative filtering method based on graph neural network

Abstract

An essential component of contemporary computer application technology is the recommender system. The collaborative filtering is one of RS's most crucial elements. acquiring knowledge about vector representations or, the model benefits from the combination of the graph neural network and model-based collaborative filtering since it can calculate the high-order connectivity in the item-user graph and perform better overall. This connectivity successfully and explicitly introduces the collaboration signal into the embedding process. Therefore, better embeddings also imply greater performance compared to more established collaborative filtering techniques, such as matrix factorization. The neural graph collaborative filtering (NGCF) algorithm will be primarily introduced in this article. In this paper, the performance of the NGCF algorithm is verified on several data sets, and the experimental results show that there is still room for improvement in the process of practical application. For instance, the NGCF algorithm is not appropriate for processing complicated data, and user cold start is an issue. This study offers a remedy for the difficulties the NGCF algorithm ran into in real-world use. Research on how to enhance the NGCF algorithm considering the issues will continue.