Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning

Ding Zou, Wei Wei*, Ziyang Wang, Xian Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

62 引用 (Scopus)
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摘要

Incorporating Knowledge Graphs (KG) into recommeder system as side information has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, from the following aspects: 1) the sparse interaction, itself, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) further results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism, to alleviate the aforementioned challenges. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction"action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each local/non-local graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/KGIC.

源语言英语
主期刊名CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
出版商Association for Computing Machinery
2817-2826
页数10
ISBN(电子版)9781450392365
DOI
出版状态已出版 - 17 10月 2022
活动31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, 美国
期限: 17 10月 202221 10月 2022

出版系列

姓名International Conference on Information and Knowledge Management, Proceedings

会议

会议31st ACM International Conference on Information and Knowledge Management, CIKM 2022
国家/地区美国
Atlanta
时期17/10/2221/10/22

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引用此

Zou, D., Wei, W., Wang, Z., Mao, X. L., Zhu, F., Fang, R., & Chen, D. (2022). Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning. 在 CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management (页码 2817-2826). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3511808.3557358