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

Kachun Lo, Tsukasa Ishigaki

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
出版社Association for Computing Machinery, Inc
ページ2076-2080
ページ数5
ISBN(電子版)9781450380379
DOI
出版ステータス出版済み - 2021 7月 11
イベント44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, カナダ
継続期間: 2021 7月 112021 7月 15

出版物シリーズ

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

会議

会議44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
国/地域カナダ
CityVirtual, Online
Period21/7/1121/7/15

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