research-article

GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

Authors:

Aravind Sankar,

Hari SundaramAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1279 - 1288

https://doi.org/10.1145/3397271.3401116

Published: 25 July 2020 Publication History

Abstract

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group.

We make two contributions. First, we present a recommender architecture-agnostic framework GroupIM that can integrate arbitrary neural preference encoders and aggregators for ephemeral group recommendation. Second, we regularize the user-group latent space to overcome group interaction sparsity by: maximizing mutual information between representations of groups and group members; and dynamically prioritizing the preferences of highly informative members through contextual preference weighting. Our experimental results on several real-world datasets indicate significant performance improvements (31-62% relative NDCG@20) over state-of-the-art group recommendation techniques.

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Cited By

Gao LZhang HFu L(2024)Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group RecommendationApplied Sciences10.3390/app1410415514:10(4155)Online publication date: 14-May-2024
https://doi.org/10.3390/app14104155
Wen JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Gaussian Mutual Information Maximization for Efficient Graph Self-Supervised Learning: Bridging Contrastive-based to Decorrelation-basedProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680682(1612-1621)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680682
Emamgholizadeh HDelić ARicci F(2024)Predicting Group Choices from Group ProfilesACM Transactions on Interactive Intelligent Systems10.1145/363971014:1(1-27)Online publication date: 10-Jan-2024
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Index Terms

GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
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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Published In

cover image ACM Conferences

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

Copyright © 2020 ACM.

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Published: 25 July 2020

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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

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

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Cited By

Gao LZhang HFu L(2024)Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group RecommendationApplied Sciences10.3390/app1410415514:10(4155)Online publication date: 14-May-2024
https://doi.org/10.3390/app14104155
Wen JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Gaussian Mutual Information Maximization for Efficient Graph Self-Supervised Learning: Bridging Contrastive-based to Decorrelation-basedProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680682(1612-1621)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680682
Emamgholizadeh HDelić ARicci F(2024)Predicting Group Choices from Group ProfilesACM Transactions on Interactive Intelligent Systems10.1145/363971014:1(1-27)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3639710
Guo LZhu YGao MTao YYu JChen CBaeza-Yates RBonchi F(2024)Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672056(944-955)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672056
Xu JChen ZLi JYang SWang HNgai ESerra ESpezzano F(2024)AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679697(2682-2691)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679697
Yu YSugiyama KJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657770(1062-1072)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657770
Zhao YZhang HBai QNie CYuan XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657699(914-923)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657699
Liang RZhang QWang JLu J(2024)A Hierarchical Attention Network for Cross-Domain Group RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320048035:3(3859-3873)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3200480
Yu JYin HXia XChen TLi JHuang Z(2024)Self-Supervised Learning for Recommender Systems: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328290736:1(335-355)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282907
Wang NLiu DZeng JMu LLi J(2024)HGRec: Group Recommendation With Hypergraph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.336384311:3(4214-4225)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3363843
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