X-2ch: Quad-Channel Collaborative Graph Network over Knowledge-Embedded Edges

Kachun Lo, Tsukasa Ishigaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Carrying abundant side information, knowledge graph (KG) has shown its great potential in enriching the sparsity of collaborative filtering (CF) for recommendation. Although graph neural networks (GNNs) have been successfully employed to learn user preferences from KG and CF signals simultaneously, most models suffer from inferior performance due to their deficient designs, i.e., 1) formulating no distinction between users, items and KG entities, 2) confounding KG signals with CF signals and 3) completely neglecting the effects of edges, which is vital for graph information propagation. In this paper, we propose a quad-channel graph model (X-2ch) to tackle these problems. First, rather than lodging KG entities on graph as nodes, X-2ch distills KG information and embeds them as edge attributes in a bi-directional manner to model the natural user-item interaction process. Second, X-2ch introduces a novel quad-channel learning scheme, including a collaborative user-item update and a CF-KG attentive propagation, to holistically capture the interconnectivity of users and items while preserving their distinct properties. Experiments on two real-world benchmarks show substantial improvement over the state-of-the-art baselines.

Original languageEnglish
Title of host publicationSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages2076-2080
Number of pages5
ISBN (Electronic)9781450380379
DOIs
Publication statusPublished - 2021 Jul 11
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
Duration: 2021 Jul 112021 Jul 15

Publication series

NameSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Country/TerritoryCanada
CityVirtual, Online
Period21/7/1121/7/15

Keywords

  • collaborative filtering
  • knowledge graph
  • personalized recommendation
  • recommender systems

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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