Abstract
With the increasing development of information technology and the rise of big data, the Internet has entered the era of information overload. While users enjoy the convenience brought by big data to their daily lives, they also face more and more information filtering and selection problems. In this context, recommendation systems have emerged, and existing recommendation systems cannot effectively deal with the problem of data sparsity. Therefore, this paper proposes a graph convolutional network based on matrix factorization for recommendation. The embedding layer uses matrix factorization instead of neighborhood aggregation, and the interaction layer uses multi-layer neural networks instead of simple inner products. Finally, on the Movielens-1M, Yelp and Gowalla public data set, NDCG and Recall are better than the existing baseline model, which effectively alleviates the data sparsity problem.
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