research-article

Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

Authors:

Lucas Vinh Tran,

Tuan-Anh Nguyen Pham,

Xiaoli LiAuthors Info & Claims

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

Pages 255 - 264

https://doi.org/10.1145/3331184.3331251

Published: 18 July 2019 Publication History

Abstract

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e., a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine-grained interactions between group members. In our MoSAN model, each sub-attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub-Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state-of-the-art performance but also improves standard baselines by a considerable margin.

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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

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Published: 18 July 2019

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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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Gan DGao MLi WWang ZGuo LJiang FSong Y(2025)LARGEExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125416260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125416
Zhou YLi QChu HLi JWei BZhang SHan J(2025)Spatiotemporal-view member preference contrastive representation learning for group recommendationMachine Learning10.1007/s10994-024-06655-3114:3Online publication date: 11-Feb-2025
https://doi.org/10.1007/s10994-024-06655-3
Wei FChen S(2024)Multi-View Collaborative Training and Self-Supervised Learning for Group RecommendationMathematics10.3390/math1301006613:1(66)Online publication date: 27-Dec-2024
https://doi.org/10.3390/math13010066
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
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
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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
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Lin SChen CHuang S(2024)Member-Augmented Group Recommendation With Multi-Interest Framework and Knowledge Graph EmbeddingsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332273211:3(3193-3206)Online publication date: Jun-2024
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