research-article

Hypercomplex Graph Collaborative Filtering

Authors:

Farookh HussainAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1914 - 1922

https://doi.org/10.1145/3485447.3512065

Published: 25 April 2022 Publication History

Abstract

Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships.

To bridge these gaps, in this paper, we propose a novel recommendation framework named HyperComplex Graph Collaborative Filtering (HCGCF). To study the high-dimensional hypercomplex algebras, we introduce Cayley–Dickson construction which utilizes a recursive process to define hypercomplex algebras and their mathematical operations. Based on Cayley–Dickson construction, we devise a hypercomplex graph convolution operator to learn user and item representations. Specifically, the operator models both the neighborhood summary and interaction relations with neighbors in hypercomplex spaces, effectively exploiting the high-order connectivity in the user-item bipartite graph. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been explicitly discussed and used in hypercomplex recommender systems. Compared with several state-of-the-art recommender baselines, HCGCF achieves superior performance in both click-through rate prediction and top-K recommendation on three real-world datasets.

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Cited By

Li AYang BHuo HHussain FXu G(2025)Self-supervised dual graph learning for recommendationKnowledge-Based Systems10.1016/j.knosys.2025.112967310(112967)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.112967
Ma JBian KXu YZhu L(2024)ANAGL: A Noise-Resistant and Anti-Sparse Graph Learning for Micro-Video RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367040720:9(1-15)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670407
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Show More Cited By

Index Terms

Hypercomplex Graph Collaborative Filtering
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Jilin Province Key Scientific and Technological Research and Development Project
Jilin Province Natural Science Foundation
the National Natural Science Foundation of China
the National Key R&D Program of China

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Li AYang BHuo HHussain FXu G(2025)Self-supervised dual graph learning for recommendationKnowledge-Based Systems10.1016/j.knosys.2025.112967310(112967)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.112967
Ma JBian KXu YZhu L(2024)ANAGL: A Noise-Resistant and Anti-Sparse Graph Learning for Micro-Video RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367040720:9(1-15)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670407
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Li AYang BHuo HHussain FXu G(2024)Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00048(544-556)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00048
Liu LHuang FLiu XXiong ZLi MSong CZhang WElkind E(2023)Multi-view contrastive learning hypergraph neural network for drug-microbe-disease association predictionProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/537(4829-4837)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/537
Jin YBai YZhu YSun YWang W(2023)Code Recommendation for Open Source Software DevelopersProceedings of the ACM Web Conference 202310.1145/3543507.3583503(1324-1333)Online publication date: 30-Apr-2023
https://doi.org/10.1145/3543507.3583503

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