short-paper

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

Authors:

Philip S. YuAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3513 - 3517

https://doi.org/10.1145/3459637.3482092

Published: 30 October 2021 Publication History

Abstract

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold-start issue, which can be alleviated if incorporating Knowledge Graphs (KGs) as side information. However, most existing works neglect the facts that node degrees in KGs are skewed and massive amount of interactions in KGs are recommendation-irrelevant. To address these problems, in this paper, we propose Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance distribution of connected items from KGs and samples suitable items for recommendation following this distribution. We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems. The code is available online at https://github.com/YuWang-1024/DSKReG.

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Cited By

Zhang HWang DSun ZLi YSun YLiang HWang W(2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
https://doi.org/10.1145/3713071
Song SLi PDun MZhang YCao HYe X(2025)SPMGAE: Self-purified masked graph autoencoders release robust expression powerNeurocomputing10.1016/j.neucom.2024.128631611(128631)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128631
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Show More Cited By

Index Terms

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhang HWang DSun ZLi YSun YLiang HWang W(2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
https://doi.org/10.1145/3713071
Song SLi PDun MZhang YCao HYe X(2025)SPMGAE: Self-purified masked graph autoencoders release robust expression powerNeurocomputing10.1016/j.neucom.2024.128631611(128631)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128631
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Yang FPeng D(2024)Spatio-Temporal Contrastive Heterogeneous Graph Attention Networks for Session-Based RecommendationMathematics10.3390/math1208119312:8(1193)Online publication date: 16-Apr-2024
https://doi.org/10.3390/math12081193
Soularidis AKotis KVouros G(2024)Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support SystemsElectronics10.3390/electronics1303052613:3(526)Online publication date: 28-Jan-2024
https://doi.org/10.3390/electronics13030526
Jiang JGuo PXu XWu JCui Y(2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3665503
Yang ZDing MHuang TCen YSong JXu BDong YTang J(2024)Does Negative Sampling Matter? a Review With Insights Into its Theory and ApplicationsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337147346:8(5692-5711)Online publication date: Aug-2024
https://doi.org/10.1109/TPAMI.2024.3371473
Zheng YWei PChen ZTang CLin L(2024)Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327639535:10(14233-14246)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3276395
Wang YHuang XMa JJin Q(2024)LASGRec: A Personalized Recommender Based on Learnable Attribute Sampling and Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.331143311:2(2930-2939)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3311433
Wang LQi YSun ZLi X(2024)MKNBLNeurocomputing10.1016/j.neucom.2024.127277575:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127277
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