Automatic Meta-Path Discovery for Effective Graph-Based Recommendation

Wentao Ning, Reynold Cheng, Jiajun Shen, Nur Al Hasan Haldar, Ben Kao, Xiao Yan, Nan Huo, Wai Kit Lam, Tian Li, Bo Tang

Research output: Chapter in Book/Conference paperConference paperpeer-review

15 Citations (Scopus)
285 Downloads (Pure)

Abstract

Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. RMS is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by RMS consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.
Original languageEnglish
Title of host publicationCIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1563-1572
Number of pages10
ISBN (Electronic)978-1-4503-9236-5
DOIs
Publication statusPublished - 17 Oct 2022
Event31st ACM International Conference on Information and Knowledge Management , CIKM 2022: CIKM2022 -
Duration: 17 Oct 202221 Oct 2022
https://www.cikm2022.org/

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
ISSN (Print)2155-0751

Conference

Conference31st ACM International Conference on Information and Knowledge Management , CIKM 2022
Period17/10/2221/10/22
Internet address

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