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ConsRec: Learning Consensus Behind Interactions for Group Recommendation

Authors:

Philip S. YuAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 240 - 250

https://doi.org/10.1145/3543507.3583277

Published: 30 April 2023 Publication History

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Abstract

Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups’ preferences via aggregating diverse members’ interests. Actually, groups’ ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions.

To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.

References

[1]

[1] Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawla, Gautam Das, and Cong Yu. 2009. Group Recommendation: Semantics and Efficiency. Proc. VLDB Endow. 2 (2009), 754–765.

Digital Library

[2]

[2] Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010. Group recommendations with rank aggregation and collaborative filtering. In RecSys ’10.

[3]

[3] Ludovico Boratto and Salvatore M. Carta. 2011. State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups. In Information Retrieval and Mining in Distributed Environments.

[4]

[4] Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018. Attentive Group Recommendation. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (2018).

[5]

[5] Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, and Jiao Xu. 2021. Social-Enhanced Attentive Group Recommendation. TKDE 33 (2021), 1195–1209.

[6]

[6] Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, and Meng Wang. 2022. Thinking inside The Box: Learning Hypercube Representations for Group Recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2022).

Digital Library

[7]

[7] Dian Cheng, Jiawei Chen, Wen-Chih Peng, Wen Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, and Xiangnan He. 2022. IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search. Proceedings of the ACM Web Conference 2022 (2022).

Digital Library

[8]

[8] Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In RecSys ’10.

[9]

[9] Yifan Feng, Haoxuan You, Zizhao Zhang, R. Ji, and Yue Gao. 2019. Hypergraph Neural Networks. ArXiv abs/1809.09401 (2019).

[10]

[10] Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS.

[11]

[11] Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. 2022. Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation. ACM Trans. Inf. Syst. 40 (2022), 3:1–3:27.

Digital Library

[12]

[12] Lei Guo, Hongzhi Yin, Qinyong Wang, Bin Cui, Zi Huang, and Li zhen Cui. 2020. Group Recommendation with Latent Voting Mechanism. 2020 IEEE 36th International Conference on Data Engineering (ICDE) (2020), 121–132.

[13]

[13] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).

Digital Library

[14]

[14] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web (2017).

Digital Library

[15]

[15] Zhixiang He, Chi-Yin Chow, and Jiadong Zhang. 2020. GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).

Digital Library

[16]

[16] Renqi Jia, Xiaofei Zhou, Linhua Dong, and Shirui Pan. 2021. Hypergraph Convolutional Network for Group Recommendation. 2021 IEEE International Conference on Data Mining (ICDM) (2021), 260–269.

[17]

[17] Thomas Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. ArXiv abs/1609.02907 (2017).

[18]

[18] Congcong Li, Yucheng Dong, and Francisco Herrera. 2019. A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups. IEEE Transactions on Fuzzy Systems 27 (2019), 221–233.

[19]

[19] Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI.

[20]

[20] Xingjie Liu, Yuan Tian, Mao Ye, and Wang-Chien Lee. 2012. Exploring personal impact for group recommendation. Proceedings of the 21st ACM international conference on Information and knowledge management (2012).

Digital Library

[21]

[21] Yujia Liu, Changyong Liang, Francisco Chiclana, and Jian Wu. 2017. A trust induced recommendation mechanism for reaching consensus in group decision making. Knowl. Based Syst. 119 (2017), 221–231.

Digital Library

[22]

[22] Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical Gating Networks for Sequential Recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019).

Digital Library

[23]

[23] André F. T. Martins and Ramón Fernández Astudillo. 2016. From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification. In ICML.

[24]

[24] Liqiang Nie, Meng Wang, Yue Gao, Zhengjun Zha, and Tat-Seng Chua. 2013. Beyond Text QA: Multimedia Answer Generation by Harvesting Web Information. IEEE Transactions on Multimedia 15 (2013), 426–441.

Digital Library

[25]

[25] Liqiang Nie, Yi-Liang Zhao, Xiangyu Wang, Jialie Shen, and Tat-Seng Chua. 2014. Learning to Recommend Descriptive Tags for Questions in Social Forums. ACM Trans. Inf. Syst. 32 (2014), 5:1–5:23.

Digital Library

[26]

[26] Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020. Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings. ArXiv abs/2002.05969 (2020).

[27]

[27] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. ArXiv abs/1205.2618 (2009).

[28]

[28] Frank Rosenblatt. 1963. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS. American Journal of Psychology 76 (1963), 705.

[29]

[29] Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao Yang, and H. Sundaram. 2020. GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).

Digital Library

[30]

[30] Young-Duk Seo, Young-Gab Kim, Euijong Lee, Kwangsoo Seol, and Doo-Kwon Baik. 2018. An enhanced aggregation method considering deviations for a group recommendation. Expert Syst. Appl. 93 (2018), 299–312.

Digital Library

[31]

[31] Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, G. Cong, and Xiaoli Li. 2019. Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019).

Digital Library

[32]

[32] Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS.

[33]

[33] Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, and James Caverlee. 2020. Next-item Recommendation with Sequential Hypergraphs. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).

Digital Library

[34]

[34] Felix Wu, Tianyi Zhang, Amauri H. de Souza, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. ArXiv abs/1902.07153 (2019).

[35]

[35] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021).

Digital Library

[36]

[36] Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Pratim Talukdar. 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. In NeurIPS.

[37]

[37] Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. Proceedings of the Web Conference 2021 (2021).

Digital Library

[38]

[38] Quan Yuan, G. Cong, and Chin-Yew Lin. 2014. COM: a generative model for group recommendation. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (2014).

Digital Library

[39]

[39] Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, and Hongzhi Yin. 2021. Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation. Proceedings of the 30th ACM International Conference on Information & Knowledge Management (2021).

Digital Library

[40]

[40] Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, and Wenwu Ou. 2021. Learning User Representations with Hypercuboids for Recommender Systems. Proceedings of the 14th ACM International Conference on Web Search and Data Mining (2021).

Digital Library

[41]

[41] Kun Zhou, Haibo Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji rong Wen. 2020. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020).

Digital Library

Cited By

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
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
Show More Cited By

Index Terms

ConsRec: Learning Consensus Behind Interactions for 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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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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National Natural Science Foundation of China
NSF (National Science Foundation)

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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
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
Zhong SHuang ZLi DWen WQin JLin LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local MinimaProceedings of the ACM Web Conference 202410.1145/3589334.3645553(3700-3711)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645553
Yang WXu JZhou RChen LLi JZhao PLiu C(2024)Multi-view Attentive Variational Learning for Group Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00381(5022-5034)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00381
Li JZhu S(2024)Multi-View Interactive Compromise Learning for Group RecommendationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10445991(9396-9400)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10445991
Hong TIbrahim N(2024)Group recommendation fueled by noise-based graph contrastive learningJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10206336:5(102063)Online publication date: Jun-2024
https://doi.org/10.1016/j.jksuci.2024.102063
Hong JYang WChao PFang J(2024)GroupMO: a memory-augmented meta-optimized model for group recommendationWorld Wide Web10.1007/s11280-024-01267-227:3Online publication date: 18-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01267-2
Yang QWang YWu ZZhang JLiu LZhang J(2024)Potential factors-embedding group recommendation for online educationDiscover Computing10.1007/s10791-024-09439-427:1Online publication date: 9-May-2024
https://doi.org/10.1007/s10791-024-09439-4
Yang XXie CZhou KSong SYang JLi B(2024)Towards attributed graph clustering using enhanced graph and reconstructed graph structureArtificial Intelligence Review10.1007/s10462-024-10958-157:11Online publication date: 30-Sep-2024
https://doi.org/10.1007/s10462-024-10958-1
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