Simplifying graph-based collaborative filtering for recommendation

Li HE, Xianzhi WANG, Dingxian WANG, Haoyuan ZOU, Hongzhi YIN, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

5 Citations (Scopus)

Abstract

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets. Copyright © 2023 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages60-68
ISBN (Electronic)9781450394079
DOIs
Publication statusPublished - Feb 2023

Citation

He, L., Wang, X., Wang, D., Zou, H., Yin, H., & Xu, G. (2023). Simplifying graph-based collaborative filtering for recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 60-68). Association for Computing Machinery. https://doi.org/10.1145/3539597.3570451

Keywords

  • Collaborative filtering
  • Recommendation
  • Embedding propagation
  • Graph Convolutional Network

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