ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development

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A Collaborative Recommendation Model Based on Enhanced Graph Convolutional Network

Wang Lei, Xiong Yuning, Li Yunpeng, Liu Yuanyuan   

  1. School of Economics Information Engineering, Southwestern University of Finance & Economics, Chengdu 610074)
  • Supported by: 
    This work was supported by the Research Planning Fund of Humanities and Social Sciences of the Ministry of Education (16XJAZH002).

Abstract: Graph convolutional network is a deep learning model for graph structure data,  and it has become a very hot approach in the research of recommendation system due to its powerful ability in feature extraction and representation of data. This paper focuses on the rating prediction tasks in recommendation system, and points out two deficiencies of existing graph convolutional network based recommendation models by detailed analysis, including that making use of first-order collaborative signals only and ignoring the differences of user opinions. For solving them, an end-to-end enhanced graph convolutional network based collaborative recommendation model is proposed. It adopts an enhanced graph convolutional layer to take full advantage of collaborative signals to learn embeddings of users and items on graph, which aggregates second-order collaborative signals and incorporates the influence of different user opinions. And it also stacks several graph convolutional layers to iteratively refine the embeddings and finally uses a nonlinear multilayer perceptron network to make rating prediction. The experiments on 5 benchmark recommendation datasets show that the proposed model achieves lower prediction errors compared with several state-of-the-art graph convolutional network based recommendation models.

Key words: collaborative recommendation, graph convolutional network, collaborative signal, user opinion, rating prediction