research-article

Geometric Disentangled Collaborative Filtering

Authors:

Liangjie Zhang,

Qi ZhangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 80 - 90

https://doi.org/10.1145/3477495.3531982

Published: 07 July 2022 Publication History

Abstract

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.

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Cited By

Shi CZhu MYu YWang XDu J(2024)Unifying Graph Neural Networks with a Generalized Optimization FrameworkACM Transactions on Information Systems10.1145/366085242:6(1-32)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660852
Liu XMeng SLi QLiu QHe QRamesh DQi L(2024)FDGNN: Feature-Aware Disentangled Graph Neural Network for RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325998311:1(1372-1383)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3259983
Chen CChi XBeheshti AChen JDou W(2024)DISS-CF: Direct Item Session Similarity Enhanced Collaborative Filtering Method for Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00054(320-329)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00054
Show More Cited By

Index Terms

Geometric Disentangled Collaborative Filtering
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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Published: 07 July 2022

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the National Natural Science Foundation of China
the National Natural Science Foundation of China

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Shi CZhu MYu YWang XDu J(2024)Unifying Graph Neural Networks with a Generalized Optimization FrameworkACM Transactions on Information Systems10.1145/366085242:6(1-32)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660852
Liu XMeng SLi QLiu QHe QRamesh DQi L(2024)FDGNN: Feature-Aware Disentangled Graph Neural Network for RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325998311:1(1372-1383)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3259983
Chen CChi XBeheshti AChen JDou W(2024)DISS-CF: Direct Item Session Similarity Enhanced Collaborative Filtering Method for Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00054(320-329)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00054
Yang XXu XXu ZChu JHu J(2024)DiffGCL: Diffusion model-based Graph Contrastive Learning for Service Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00053(310-319)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00053
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
Gong JZhao YZhao JZhang JMa GZheng SDu STang J(2024)Personalized recommendation via inductive spatiotemporal graph neural networkPattern Recognition10.1016/j.patcog.2023.109884145:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.109884
Zhang MJiang MTao XWang KKong J(2024)Disentangled representation learning for collaborative filtering based on hyperbolic geometryKnowledge-Based Systems10.1016/j.knosys.2023.111135282:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111135
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
Zhao YJu JGong JZhao JChen MChen LFeng XPeng J(2024)Cross-domain recommendation via adaptive bi-directional transfer graph neural networksKnowledge and Information Systems10.1007/s10115-024-02246-9Online publication date: 30-Sep-2024
https://doi.org/10.1007/s10115-024-02246-9
Wu WZhai X(2023)DyLFG: A Dynamic Network Learning Framework Based on GeometryEntropy10.3390/e2512161125:12(1611)Online publication date: 30-Nov-2023
https://doi.org/10.3390/e25121611
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