research-article

Simplifying Graph-based Collaborative Filtering for Recommendation

Authors:

Guandong XuAuthors Info & Claims

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

Pages 60 - 68

https://doi.org/10.1145/3539597.3570451

Published: 27 February 2023 Publication History

Abstract

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets.

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Cited By

Haghir Chehreghani M(2024)A Review on the Impact of Data Representation on Model ExplainabilityACM Computing Surveys10.1145/366217856:10(1-21)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3662178
Hwang JJu HKang SJang SYu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-Domain Sequential Recommendation via Domain Space LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657685(2134-2144)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657685
Hao YZhao PQu JZhao LLiu GZhuang FSheng VZhou X(2024)Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00058(679-691)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00058
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Index Terms

Simplifying Graph-based Collaborative Filtering for Recommendation
1. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis

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

February 27 - March 3, 2023

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Cited By

Haghir Chehreghani M(2024)A Review on the Impact of Data Representation on Model ExplainabilityACM Computing Surveys10.1145/366217856:10(1-21)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3662178
Hwang JJu HKang SJang SYu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-Domain Sequential Recommendation via Domain Space LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657685(2134-2144)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657685
Hao YZhao PQu JZhao LLiu GZhuang FSheng VZhou X(2024)Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00058(679-691)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00058
Liu ZMeng LSheng QChu DYu JSong X(2024)POI recommendation for random groups based on cooperative graph neural networksInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10367661:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103676
Singhal SPal K(2024)State of art and emerging trends on group recommender system: a comprehensive reviewInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00329-513:2Online publication date: 2-May-2024
https://doi.org/10.1007/s13735-024-00329-5
Wang TQin JMa C(2024)Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregationNeural Computing and Applications10.1007/s00521-024-09795-8Online publication date: 2-May-2024
https://doi.org/10.1007/s00521-024-09795-8
Li XWu ZZhang WZhu YLi RWang G(2023)FedGTA: Topology-Aware Averaging for Federated Graph LearningProceedings of the VLDB Endowment10.14778/3617838.361784217:1(41-50)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.14778/3617838.3617842
Ong RQiu WKhong AFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615020(1949-1959)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615020

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