关键词: 推荐系统, 信息冲突, 图神经网络, 三支决策

Abstract:

With increasing in scales of social networks, modeling complex graphs come to be a challenge for cold start recommendations. The conflicts involving in these complex information will directly affect the recommendation results. So the paper proposes a Graph Neural Networks recommendation algorithm based on Three-Way Decision theory, named as TWD-GNN. Three-way decision is introduced to divide the entire data set into tri-partitions as positive, boundary, and negative domains. And solving local conflicts can benefit from the progress, which the auxiliary information from multi-sources is integrated with the boundary domain. The graph neural network method is available for effectively mining information, reconstructing the network and predicting the scores for recommendations. The experimental results show that the proposed algorithm can reflect user preferences more accurately than the traditional collaborative filtering recommendation algorithms, and and improve the recommendation quality.

Key words: recommendation system, information conflict, graph neural network, three-way decision