MGRF: Multi-Graph Recommendation Framework with Heterogeneous and Homogeneous Graph Iterative Fusion

Authors

  • Xiang Lin School of Information Engineering, Xuzhou College of Industrial Technology, Xuzhou, Jiangsu, 221140, China & School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
  • Fangyu Han School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
  • Xiaobin Rui School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
  • Chengcheng Sun School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
  • Zhixiao Wang School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
  • Lijun Yan College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Xuhui, Shanghai, 200234, China

DOI:

https://doi.org/10.31577/cai_2024_3_687

Keywords:

Recommender systems, multi-graph fusion, graph neural networks, embedding propagation

Abstract

With the development of deep learning, deep neural methods have been introduced to boost the performance of Collaborative Filtering (CF) models. However, most of the models rely solely on the user-item heterogeneous graph and only implicitly capture homogenous information, which limits their performance improvement. Although some state-of-the-art methods try to utilize additional graphs to make up, they either merely aggregate the information of multiple graphs in the step of initial embedding or only merge different multi-graph information in the step of final embedding. Such one-time multi-graph integration leads to the loss of interactive and topological information in the intermediate process of propagation. This paper proposes a novel Multi-Graph iterative fusion Recommendation Framework (MGRF) for CF recommendation. The core components are dual information crossing interaction and multi-graph fusing propagation. The former enables repeated feature crossing between heterogeneous nodes throughout the whole embedding process. The latter repeatedly integrates homogeneous nodes as well as their topological relationships based on the constructed user-user and item-item graphs. Thus, MGRF can improve the embedding quality by iteratively fusing user-item heterogeneous graph, user-user and item-item homogeneous graphs. Extensive experiments on three public benchmarks demonstrate the effectiveness of MGRF, which outperforms state-of-the-art baselines in terms of Recall and NDCG.

 

Downloads

Download data is not yet available.

Downloads

Published

2024-06-24

How to Cite

Lin, X., Han, F., Rui, X., Sun, C., Wang, Z., & Yan, L. (2024). MGRF: Multi-Graph Recommendation Framework with Heterogeneous and Homogeneous Graph Iterative Fusion. Computing and Informatics, 43(3), 687–708. https://doi.org/10.31577/cai_2024_3_687

Most read articles by the same author(s)