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DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation

Published: 11 July 2024 Publication History

Abstract

Group recommendation aims to suggest items to a group of users that are suitable for the group. Although some existing powerful deep learning models have achieved improved performance, various aspects remain unexplored: (1) Most existing models using contrastive learning tend to rely on high-quality data augmentation which requires precise contrastive view generation; (2) There is multifaceted natural noise in group recommendation, and additional noise is introduced during data augmentation; (3) Most existing hypergraph neural network-based models over-entangle the information of members and items, ignoring their unique characteristics. In light of this, we propose a highly effective <u>D</u>isentangled <u>H</u>ypergraph <u>M</u>asked <u>A</u>uto <u>E</u>ncoder-enhanced method for group recommendation (DHMAE), combining a disentangled hypergraph neural network with a graph masked autoencoder. This approach creates self-supervised signals without data augmentation by masking the features of some nodes and hyperedges and then reconstructing them. For the noise problem, we design a masking strategy that relies on pre-computed degree-sensitive probabilities for the process of masking features. Furthermore, we propose a disentangled hypergraph neural network for group recommendation scenarios to extract common messages of members and items and disentangle them during the convolution process. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art models and effectively addresses the noise issue.

References

[1]
Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawlat, Gautam Das, and Cong Yu. 2009. Group recommendation: Semantics and efficiency. Proceedings of the VLDB Endowment, Vol. 2, 1 (2009), 754--765.
[2]
Song Bai, Feihu Zhang, and Philip HS Torr. 2021. Hypergraph convolution and hypergraph attention. Pattern Recognition, Vol. 110 (2021), 107637.
[3]
Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems. 119--126.
[4]
Karim Benouaret and Kian-Lee Tan. 2023. Probabilistic Majority Rule-Based Group Recommendation. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 1489--1501.
[5]
Ludovico Boratto and Salvatore 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. Springer, 1--20.
[6]
Alain Bretto. 2013. Hypergraph theory. An introduction. Mathematical Engineering. Cham: Springer, Vol. 1 (2013).
[7]
Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018. Attentive group recommendation. In The 41st International ACM SIGIR conference on research & development in information retrieval. 645--654.
[8]
Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, and Jiao Xu. 2019. Social-enhanced attentive group recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 3 (2019), 1195--1209.
[9]
Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, and Meng Wang. 2022b. Thinking inside the box: learning hypercube representations for group recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1664--1673.
[10]
Yabo Chen, Yuchen Liu, Dongsheng Jiang, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong, and Qi Tian. 2022a. Sdae: Self-distillated masked autoencoder. In European Conference on Computer Vision. Springer, 108--124.
[11]
Sriharsha Dara, C Ravindranath Chowdary, and Chintoo Kumar. 2020. A survey on group recommender systems. Journal of Intelligent Information Systems, Vol. 54, 2 (2020), 271--295.
[12]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[13]
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249--256.
[14]
Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. 2021. Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Transactions on Information Systems (TOIS), Vol. 40 (2021), 1--27.
[15]
Lei Guo, Hongzhi Yin, Qinyong Wang, Bin Cui, Zi Huang, and Lizhen Cui. 2020. Group recommendation with latent voting mechanism. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 121--132.
[16]
Bowen Hao, Hongzhi Yin, Cuiping Li, and Hong Chen. 2022. Self-supervised graph learning for occasional group recommendation. International Journal of Intelligent Systems, Vol. 37, 12 (2022), 10880--10902.
[17]
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 16000--16009.
[18]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision. 1026--1034.
[19]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.
[20]
Zhixiang He, Chi-Yin Chow, and Jia-Dong Zhang. 2020. GAME: Learning graphical and attentive multi-view embeddings for occasional group recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 649--658.
[21]
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2016. beta-vae: Learning basic visual concepts with a constrained variational framework. In International conference on learning representations.
[22]
Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, and Jie Tang. 2023. GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. In Proceedings of the ACM Web Conference 2023. 737--746.
[23]
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. Graphmae: Self-supervised masked graph autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 594--604.
[24]
Jianye Ji, Jiayan Pei, Shaochuan Lin, Taotao Zhou, Hengxu He, Jia Jia, and Ning Hu. 2023. Multi-Granularity Attention Model for Group Recommendation. arXiv preprint arXiv:2308.04017 (2023).
[25]
Renqi Jia, Xiaofei Zhou, Linhua Dong, and Shirui Pan. 2021. Hypergraph convolutional network for group recommendation. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 260--269.
[26]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[27]
Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 1748--1757.
[28]
Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, and Zhi Liu. 2023. Towards reliable item sampling for recommendation evaluation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4409--4416.
[29]
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.
[30]
Fan Liu, Huilin Chen, Zhiyong Cheng, Anan Liu, Liqiang Nie, and Mohan Kankanhalli. 2022. Disentangled multimodal representation learning for recommendation. IEEE Transactions on Multimedia (2022).
[31]
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled graph convolutional networks. In International conference on machine learning. PMLR, 4212--4221.
[32]
J Orbach. 1962. Principles of neurodynamics. Perceptrons and the theory of brain mechanisms. Archives of General Psychiatry, Vol. 7, 3 (1962), 218--219.
[33]
Yuyang Ren, Zhang Haonan, Luoyi Fu, Xinbing Wang, and Chenghu Zhou. 2023. Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1660--1669.
[34]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[35]
Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao Yang, and Hari Sundaram. 2020. Groupim: A mutual information maximization framework for neural group recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 1279--1288.
[36]
Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, and Xia Hu. 2023. S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 787--795.
[37]
Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, and Xiaoli Li. 2019. Interact and decide: Medley of sub-attention networks for effective group recommendation. In Proceedings of the 42nd International ACM SIGIR conference on research and development in information retrieval. 255--264.
[38]
Wen Wang, Wei Zhang, Jun Rao, Zhijie Qiu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Group-aware long-and short-term graph representation learning for sequential group recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1449--1458.
[39]
Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, and Philip S Yu. 2023. ConsRec: Learning Consensus Behind Interactions for Group Recommendation. In Proceedings of the ACM Web Conference 2023. 240--250.
[40]
Yaowen Ye, Lianghao Xia, and Chao Huang. 2023. Graph Masked Autoencoder for Sequential Recommendation. arXiv preprint arXiv:2305.04619 (2023).
[41]
Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019. Social influence-based group representation learning for group recommendation. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 566--577.
[42]
Quan Yuan, Gao Cong, and Chin-Yew Lin. 2014. COM: a generative model for group recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 163--172.
[43]
Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, and Hongzhi Yin. 2021. Double-scale self-supervised hypergraph learning for group recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management. 2557--2567.
[44]
Xin Zhou, Donghui Lin, Yong Liu, and Chunyan Miao. 2023. Layer-refined graph convolutional networks for recommendation. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 1247--1259.

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cover image ACM Conferences
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
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Published: 11 July 2024

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Author Tags

  1. graph neural networks
  2. group recommendation
  3. masked autoencoder
  4. self-supervised learning

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