Collaborative Filtering with Heterogeneous Neighborhood Aggregation
XIA Hongbin1,2, LU Wei1, LIU Yuan1,2
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122 2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122
Abstract In traditional collaborative filtering models, the feature vector generated by one-hot encoding is sparsely informative. Heterogeneous behavior data is only employed to describe the relationship between different behaviors and the relationship between behaviors of different users is ignored.Aiming at these problems, an algorithm of collaborative filtering with heterogeneous neighborhood aggregation is proposed. Firstly, the heterogeneous interaction between users and items is modeled by the graph, and neighborhoods are built through the connectivity of graph. Then, the neighborhood information integrated by the lightweight graph convolution method is merged into the feature vectors of the target users and items. Finally, the feature vectors of users and items integrating with neighborhood information are input into a multi-task heterogeneous network for training. The problem of data sparseness is alleviated by enriching the hidden information of feature vectors. Experiments on the datasets prove that the performance of the proposed model is better.
Fund:National Natural Science Foundation of China(No.61972182)
Corresponding Authors:
XIA Hongbin, Ph.D., associate professor. His research interests include personalized recommendation, natural language processing and network optimization.
About author:: LU Wei, master student. His research interests include deep learning and recommendation system.LIU Yuan, master, professor. His research interests include network security and social network.
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