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Spectral collaborative filtering

Authors:

Philip S. YuAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 311 - 319

https://doi.org/10.1145/3240323.3240343

Published: 27 September 2018 Publication History

Abstract

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.

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Cited By

Xu ZLi BCao W(2025)Enhancing federated learning-based social recommendations with graph attention networksNeurocomputing10.1016/j.neucom.2024.129045617(129045)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129045
Alshbanat HBenhidour HKerrache S(2025)A survey of latent factor models in recommender systemsInformation Fusion10.1016/j.inffus.2024.102905117:COnline publication date: 1-May-2025
https://dl.acm.org/doi/10.1016/j.inffus.2024.102905
Wang JXie HZhang SQin STao XWang FXu X(2025)Multimodal fusion framework based on knowledge graph for personalized recommendationExpert Systems with Applications10.1016/j.eswa.2024.126308268(126308)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126308
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Index Terms

Spectral collaborative filtering
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 ACM.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Published: 27 September 2018

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RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Xu ZLi BCao W(2025)Enhancing federated learning-based social recommendations with graph attention networksNeurocomputing10.1016/j.neucom.2024.129045617(129045)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129045
Alshbanat HBenhidour HKerrache S(2025)A survey of latent factor models in recommender systemsInformation Fusion10.1016/j.inffus.2024.102905117:COnline publication date: 1-May-2025
https://dl.acm.org/doi/10.1016/j.inffus.2024.102905
Wang JXie HZhang SQin STao XWang FXu X(2025)Multimodal fusion framework based on knowledge graph for personalized recommendationExpert Systems with Applications10.1016/j.eswa.2024.126308268(126308)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126308
Li JYu YLiu YOuyang C(2024)GCN-based Interest and Popularity Disentangled Representations Framework for RecommendationData Intelligence10.3724/2096-7004.di.2024.0031Online publication date: 22-Nov-2024
https://doi.org/10.3724/2096-7004.di.2024.0031
Dose YHaruta SHara T(2024)INCL: A Graph-based Recommendation Model Using Contrastive Learning for Inductive ScenarioJournal of Information Processing10.2197/ipsjjip.32.92932(929-937)Online publication date: 2024
https://doi.org/10.2197/ipsjjip.32.929
Liu SLi CZhang XZhao MXu YBu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Deeply Fusing Semantics and Interactions for Item Representation Learning via Topology-driven Pre-trainingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681639(9505-9514)Online publication date: 28-Oct-2024
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Wang CYang LLiu ZLiu XYang MLiang YYu PSerra ESpezzano F(2024)Collaborative Alignment for RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679535(2315-2325)Online publication date: 21-Oct-2024
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Wu WWang CShen DQin CChen LXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657724(1242-1252)Online publication date: 10-Jul-2024
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Xia LHuang CXu YDai PBo L(2024)Multi-Behavior Graph Neural Networks for Recommender SystemIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.3204775(1-15)Online publication date: 2024
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Seo CJeong KLim SShin W(2024)SiReN: Sign-Aware Recommendation Using Graph Neural NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.3175772(1-15)Online publication date: 2024
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