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Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

Authors:

Irwin KingAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 94 - 102

https://doi.org/10.1145/3488560.3498419

Published: 15 February 2022 Publication History

Abstract

Aiming to alleviate data sparsity and cold-start problems of tradi- tional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topologies to learn the low-dimensional representations of users and items with rich semantics. These real-world tripartite graphs are usually scale-free, however, the intrinsic hierarchical graph structures of which are underemphasized in existing works, consequently, leading to suboptimal recommendation performance. To address this issue and provide more accurate recommendation, we propose a knowledge-aware recommendation method with Lorentz model of the hyperbolic geometry, namely Lorentzian Knowledge-enhanced Graph convolutional networks for Recommendation (LKGR). LKGR facilitates better modeling of scale-free tripartite graphs after the data unification. Specifically, we employ different information propagation strategies in the hyperbolic space to explicitly encode heterogeneous information from historical interactions and KGs. Additionally, our proposed knowledge-aware attention mechanism enables the model to automatically measure the information contribution, producing the coherent information aggregation in the hyperbolic space. Extensive experiments on three real-world benchmarks demonstrate that LKGR outperforms state-of-the-art methods by 3.6-15.3% of Recall@20 on Top-K recommendation.

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Cited By

Hua QZhou JZhang FDong CXu D(2025)Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for RecommendationTsinghua Science and Technology10.26599/TST.2023.901014330:2(585-599)Online publication date: Apr-2025
https://doi.org/10.26599/TST.2023.9010143
Qiu ZZhu JChen YCai GLiu WDong ZKing ISerra ESpezzano F(2024)EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR PredictionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680048(4819-4827)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680048
Zhang LWu NSerra ESpezzano F(2024)HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679701(3186-3196)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679701
Show More Cited By

Index Terms

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

Copyright © 2022 ACM.

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Published: 15 February 2022

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WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

AZ, Virtual Event, USA

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Cited By

Hua QZhou JZhang FDong CXu D(2025)Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for RecommendationTsinghua Science and Technology10.26599/TST.2023.901014330:2(585-599)Online publication date: Apr-2025
https://doi.org/10.26599/TST.2023.9010143
Qiu ZZhu JChen YCai GLiu WDong ZKing ISerra ESpezzano F(2024)EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR PredictionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680048(4819-4827)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680048
Zhang LWu NSerra ESpezzano F(2024)HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679701(3186-3196)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679701
Chen YFang YZhang YMa CHong YKing I(2024)Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive HashingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342589136:12(9418-9432)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3425891
He GZhang ZWu HLuo SLiu Y(2024)KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for RecommendationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33699768:4(2736-2748)Online publication date: Aug-2024
https://doi.org/10.1109/TETCI.2024.3369976
Yu YZhang AZhang LGao RGao SYin H(2024)Hyperbolic Translation-Based Sequential RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340971111:6(7467-7483)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3409711
Zhang YLi KZhu JYuan MHuang YLi X(2024)Leveraging Hyperbolic Dynamic Neural Networks for Knowledge-Aware RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335346711:3(4396-4411)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3353467
Tu LMeng SQi LXu XZhang X(2024)Hyperbolic Contrastive Learning with Second Order Sampling for Collaborative Filtering2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00050(281-290)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00050
Tan YLv HZhou ZGuo WXiong BLiu WChen CWang SYang C(2024)Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00108(1310-1323)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00108
Qi MWu Y(2024)Weakly supervised video anomaly detection based on hyperbolic spaceScientific Reports10.1038/s41598-024-77505-414:1Online publication date: 1-Nov-2024
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