Abstract:  With the advent of big data era, the development of recommendation systems has become more and more vigorous. It has become a research hotspot to push information that may be of interest to users in a timely manner among massive amounts of information. Traditional recommendation algorithm lack implicit information and contextual information about graph structures. In response to this, a recommendation model is proposed based on graph neural network. The main innovations are: 1) Based on the higher-order connectivity theory of graphs, the graph neural network is used to mine the hidden information in the user-item bipartite graph, and a the order is extended to multiple orders, so as to obtain more accurate embedded representation and recommendation effect; 2 ) Consider context information in the update process, which is conducive to understanding the interaction between contexts. The model is tested on the Yelp-OS, Yelp-NC and Amazon-book datasets, and the results show that it is better than the related comparison algorithms in both HR(Hit Ratio)and NDCG(Normalized Discounted Cumulative Gain) indicators, which proves that the algorithm can optimize the recommendation effect and improve the recommendation quality. 

Key words: recommendation system, graph neural network, higher-order connectivity, bipartite-graph