research-article

VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation

Authors:

Han XuAuthors Info & Claims

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Pages 526 - 534

https://doi.org/10.1145/3539597.3570482

Published: 27 February 2023 Publication History

Abstract

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type independently. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that there may exist some latent relevance among relations in KG. It may not necessary nor effective to consider all relation types for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which clusters relations with latent relevance to generates virtual relations. Specifically, we first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for node encoding on VRKGs, which iteratively updates a node embedding only depending on the node itself and its neighbors, but involve no additional training parameters. LWS mechanism is also employed on a user-item bipartite graph for user representation learning, which utilizes item encodings with virtual relational knowledge to help train user representations. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

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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
Guo JYin ZFeng SYao DLiu S(2025)Dual intent view contrastive learning for knowledge aware recommender systemsScientific Reports10.1038/s41598-025-86416-x15:1Online publication date: 16-Jan-2025
https://doi.org/10.1038/s41598-025-86416-x
Chu GGuan XShi YZhang BPu D(2025)Self-augmented Contrastive Learning for Knowledge-Aware RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_17(261-276)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_17
Show More Cited By

Index Terms

VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

February 2023

1345 pages

ISBN:9781450394079

DOI:10.1145/3539597

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Hady Lauw
Singapore Management University
,
Program Chairs:
Luo Si
Salesforce
,
Evimaria Terzi
Boston University
,
Panayiotis Tsaparas
University of Ioannina

Copyright © 2023 ACM.

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Published: 27 February 2023

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National Natural Science Foundation of China

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WSDM '23

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WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining

February 27 - March 3, 2023

Singapore, Singapore

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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
Guo JYin ZFeng SYao DLiu S(2025)Dual intent view contrastive learning for knowledge aware recommender systemsScientific Reports10.1038/s41598-025-86416-x15:1Online publication date: 16-Jan-2025
https://doi.org/10.1038/s41598-025-86416-x
Chu GGuan XShi YZhang BPu D(2025)Self-augmented Contrastive Learning for Knowledge-Aware RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_17(261-276)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_17
Tang JDai SSun ZChen XXu JYu WHu LJiang PLi HBaeza-Yates RBonchi F(2024)Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671661(2854-2865)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671661
Xiong ZHankiz YAskar H(2024)Recommendation model based on knowledge graphs and semantic alignment2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825885(7242-7251)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825885
Li KZhang YZhu JLi XWang X(2024)Multi-space interaction learning for disentangled knowledge-aware recommendationExpert Systems with Applications10.1016/j.eswa.2024.124458254(124458)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124458
Ahmad WWang B(2024)A neural diffusion model for identifying influential nodes in complex networksChaos, Solitons & Fractals10.1016/j.chaos.2024.115682189(115682)Online publication date: Dec-2024
https://doi.org/10.1016/j.chaos.2024.115682
Zhang YCai JLi CLi TWang H(2024)KMPR-AEP: Knowledge-Enhanced Multi-task Parallelized Recommendation Algorithm Incorporating Attention-Embedded PropagationInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00625-217:1Online publication date: 12-Aug-2024
https://doi.org/10.1007/s44196-024-00625-2
Guo ZSong PFeng CYao KDang CLiang J(2024)Causal intervention for knowledge graph denoising in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02500-0Online publication date: 21-Dec-2024
https://doi.org/10.1007/s13042-024-02500-0
Wang WZhang XZhang JCai WZhao HZhang X(2024)MHRE: Multivariate link prediction method for medical hyper-relational factsApplied Intelligence10.1007/s10489-023-05248-254:2(1311-1334)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05248-2
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