research-article

Unified Collaborative Filtering over Graph Embeddings

Authors:

Yongfeng ZhangAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 155 - 164

https://doi.org/10.1145/3331184.3331224

Published: 18 July 2019 Publication History

Abstract

Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the most important approaches to recommender systems research, and various CF models have been designed and applied to different scenarios. However, a challenging task is how to select the most appropriate CF model for a specific recommendation task. In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem. Specifically, UGrec models user and item interactions within a graph network, and sequential recommendation path is designed as a basic unit to capture the correlations between users and items. Mathematically, we show that many representative recommendation approaches and their variants can be mapped as a recommendation path in the graph. In addition, by applying a carefully designed attention mechanism on the recommendation paths, UGrec can determine the significance of each sequential recommendation path so as to conduct automatic model selection. Compared with state-of-the-art methods, our method shows significant improvements for recommendation quality. This work also leads to a deeper understanding of the connection between graph embeddings and recommendation algorithms.

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Cited By

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
https://doi.org/10.1109/TNNLS.2022.3204775
Shang YGao CChen JJin DWang MLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Learning Fine-grained User Interests for Micro-video RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591713(433-442)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591713
Ji JZhang BYu JZhang XQiu DZhang B(2023)Relationship-aware contrastive learning for social recommendationsInformation Sciences10.1016/j.ins.2023.02.011629(778-797)Online publication date: Jun-2023
https://doi.org/10.1016/j.ins.2023.02.011
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Index Terms

Unified Collaborative Filtering over Graph Embeddings
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. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

Copyright © 2019 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

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Published: 18 July 2019

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National Natural Science Foundation of China
fundamental Research for the Central Universities

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SIGIR '19

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SIGIR

SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

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

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Cited By

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https://doi.org/10.1109/TNNLS.2022.3204775
Shang YGao CChen JJin DWang MLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Learning Fine-grained User Interests for Micro-video RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591713(433-442)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591713
Ji JZhang BYu JZhang XQiu DZhang B(2023)Relationship-aware contrastive learning for social recommendationsInformation Sciences10.1016/j.ins.2023.02.011629(778-797)Online publication date: Jun-2023
https://doi.org/10.1016/j.ins.2023.02.011
Zhang LLiu PGulla J(2023)Recommending on graphs: a comprehensive review from a data perspectiveUser Modeling and User-Adapted Interaction10.1007/s11257-023-09359-w33:4(803-888)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s11257-023-09359-w
Zhang YWu XFang QQian SXu C(2022)Knowledge-Enhanced Attributed Multi-Task Learning for Medicine RecommendationACM Transactions on Information Systems10.1145/352766241:1(1-24)Online publication date: 14-Apr-2022
https://dl.acm.org/doi/10.1145/3527662
Fang JLiang SMeng ZDe Rijke M(2022)Hyperspherical Variational Co-embedding for Attributed NetworksACM Transactions on Information Systems10.1145/347828440:3(1-36)Online publication date: 31-Jul-2022
https://dl.acm.org/doi/10.1145/3478284
Liu SWang BDeng XYang L(2022)Self-Attentive Graph Convolution Network With Latent Group Mining and Collaborative Filtering for Personalized RecommendationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31106779:5(3212-3221)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TNSE.2021.3110677
Li ZJi JFu ZGe YXu SChen CZhang Y(2021)Efficient Non-Sampling Knowledge Graph EmbeddingProceedings of the Web Conference 202110.1145/3442381.3449859(1727-1736)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449859
Fang HZhang DShu YGuo G(2020)Deep Learning for Sequential RecommendationACM Transactions on Information Systems10.1145/342672339:1(1-42)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3426723
Jiang HWang WWei YGao ZWang YNie LWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)What Aspect Do You LikeProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413653(3487-3495)Online publication date: 12-Oct-2020
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