research-article

Geometric Disentangled Collaborative Filtering

Authors:

Liangjie Zhang,

Qi ZhangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 80 - 90

https://doi.org/10.1145/3477495.3531982

Published: 07 July 2022 Publication History

Abstract

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.

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References

[1]

Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, Vol. 17, 6 (2005), 734--749.

Digital Library

[2]

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In SIGKDD.

Digital Library

[3]

Gregor Bachmann, Gary Bécigneul, and Octavian-Eugen Ganea. 2020. Constant Curvature Graph Convolutional Networks. In ICML.

[4]

Gary Becigneul and Octavian-Eugen Ganea. 2019. Riemannian Adaptive Optimization Methods. In ICLR.

[5]

Marcel Berger. 2012. A panoramic view of Riemannian geometry. Springer Science & Business Media.

[6]

Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011). In RecSys. ACM.

Digital Library

[7]

Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In NeurIPS. 4869--4880.

[8]

Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021. Curriculum Disentangled Recommendation with Noisy Multi-feedback. NeurIPS, Vol. 34 (2021).

[9]

Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. 2018. Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018).

[10]

Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018. Hyperbolic neural networks. In NeurIPS. 5350--5360.

[11]

Albert Gu, Frederic Sala, Beliz Gunel, and Christopher Ré. 2018. Learning mixed-curvature representations in product spaces. In ICLR.

[12]

F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19.

[13]

Søren Hauberg. 2018. Directional statistics with the spherical normal distribution. In FUSION. 704--711.

[14]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.

Digital Library

[15]

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.

Digital Library

[16]

Sigurdur Helgason. 1979. Differential geometry, Lie groups, and symmetric spaces. Vol. 80. Academic press.

[17]

Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In ICLR. 1024--1034.

[18]

Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In NeurIPS. 515--524.

[19]

Kuhn HW and T AW. 1951. Nonlinear programming. In Proceedings of 2nd Berkeley Symposium, Berkeley: University of California Press. 481--492.

[20]

Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2019. Disentangled Representation Learning for Non-Parallel Text Style Transfer. In ACL. 424--434.

[21]

William Karush. 1939. Minima of Functions of Several Variables with Inequalities as Side Conditions. Master's thesis. Department of Mathematics, University of Chicago, Chicago, IL, USA.

[22]

Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. ICLR (2015).

[23]

Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).

[24]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[25]

Dmitri Krioukov, Fragkiskos Papadopoulos, Maksim Kitsak, Amin Vahdat, and Marián Boguná. 2010. Hyperbolic geometry of complex networks. Physical Review E, Vol. 82, 3 (2010), 036106.

[26]

Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In SIGKDD. 305--314.

[27]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.

[28]

Nathan Linial, Eran London, and Yuri Rabinovich. 1995. The geometry of graphs and some of its algorithmic applications. Combinatorica, Vol. 15, 2 (1995), 215--245.

[29]

Qi Liu, Maximilian Nickel, and Douwe Kiela. 2019. Hyperbolic graph neural networks. In NeurIPS. 8228--8239.

[30]

Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019 a. Disentangled graph convolutional networks. In ICML. PMLR, 4212--4221.

[31]

Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019 b. Learning disentangled representations for recommendation. In NeurIPS. 5711--5722.

[32]

Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020 a. Disentangled Self-Supervision in Sequential Recommenders. In SIGKDD. 483--491.

[33]

Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020 b. Disentangled self-supervision in sequential recommenders. In SIGKDD. 483--491.

[34]

Emile Mathieu, Charline Le Lan, Chris J Maddison, Ryota Tomioka, and Yee Whye Teh. 2019. Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. arXiv preprint arXiv:1901.06033 (2019).

[35]

Emile Mathieu and Maximilian Nickel. 2020. Riemannian Continuous Normalizing Flows. In NeurIPS. 2503--2515.

[36]

Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan Oseledets, and Alexander Tuzhilin. 2020. Performance of hyperbolic geometry models on top-N recommendation tasks. In RecSys. 527--532.

[37]

Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, and Masanori Koyama. 2019. A wrapped normal distribution on hyperbolic space for gradient-based learning. In ICML. 4693--4702.

[38]

Maximillian Nickel and Douwe Kiela. 2017. Poincaré embeddings for learning hierarchical representations. In NeurIPS. 6338--6347.

[39]

Maximilian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In ICML. 3779--3788.

[40]

Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, and Guoying Zhao. 2021. Hyperbolic deep neural networks: A survey. arXiv preprint arXiv:2101.04562 (2021).

[41]

Frederic Sala, Chris De Sa, Albert Gu, and Christopher Ré. 2018. Representation tradeoffs for hyperbolic embeddings. In ICML. 4457--4466.

[42]

Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah. 2021. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. In WWW. 2535--2546.

[43]

Ondrej Skopek, Octavian-Eugen Ganea, and Gary Bécigneul. 2019. Mixed-curvature Variational Autoencoders. In ICLR.

[44]

Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009).

[45]

Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, and Maksims Volkovs. 2021. HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601.

[46]

Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, and Xiaoli Li. 2020. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. In WSDM. 609--617.

[47]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In SIGKDD. 1235--1244.

[48]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.

[49]

Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010.

[50]

Richard C Wilson, Edwin R Hancock, El.zbieta Pekalska, and Robert PW Duin. 2014. Spherical and hyperbolic embeddings of data. IEEE transactions on pattern analysis and machine intelligence, Vol. 36, 11 (2014), 2255--2269.

[51]

Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang. 2020. Factorizable Graph Convolutional Networks. NeurIPS, Vol. 33 (2020).

[52]

Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, and Guandong Xu. 2021 a. Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems. arXiv preprint arXiv:2105.08908 (2021).

[53]

Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, and Guojie Song. 2021 b. Lorentzian Graph Convolutional Networks. In WWW. 1249--1261.

[54]

Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, and Yong Li. 2020. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. arXiv preprint arXiv:2006.11011 (2020).

Cited By

Zhang YZhang YSang LSheng V(2024)Simplify to the Limit! Embedding-Less Graph Collaborative Filtering for Recommender SystemsACM Transactions on Information Systems10.1145/370123043:1(1-30)Online publication date: 19-Oct-2024
https://dl.acm.org/doi/10.1145/3701230
Shi CZhu MYu YWang XDu J(2024)Unifying Graph Neural Networks with a Generalized Optimization FrameworkACM Transactions on Information Systems10.1145/366085242:6(1-32)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660852
Liu XMeng SLi QLiu QHe QRamesh DQi L(2024)FDGNN: Feature-Aware Disentangled Graph Neural Network for RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325998311:1(1372-1383)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3259983
Show More Cited By

Index Terms

Geometric Disentangled Collaborative Filtering
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
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UAM and Amazon
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Julio Gonzalo
UNED
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Program Chairs:
Ben Carterette
Spotify
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J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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the National Natural Science Foundation of China
the National Natural Science Foundation of China

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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

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Cited By

Zhang YZhang YSang LSheng V(2024)Simplify to the Limit! Embedding-Less Graph Collaborative Filtering for Recommender SystemsACM Transactions on Information Systems10.1145/370123043:1(1-30)Online publication date: 19-Oct-2024
https://dl.acm.org/doi/10.1145/3701230
Shi CZhu MYu YWang XDu J(2024)Unifying Graph Neural Networks with a Generalized Optimization FrameworkACM Transactions on Information Systems10.1145/366085242:6(1-32)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660852
Liu XMeng SLi QLiu QHe QRamesh DQi L(2024)FDGNN: Feature-Aware Disentangled Graph Neural Network for RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325998311:1(1372-1383)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3259983
Chen CChi XBeheshti AChen JDou W(2024)DISS-CF: Direct Item Session Similarity Enhanced Collaborative Filtering Method for Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00054(320-329)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00054
Yang XXu XXu ZChu JHu J(2024)DiffGCL: Diffusion model-based Graph Contrastive Learning for Service Recommendation2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00053(310-319)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00053
Tan YLv HZhou ZGuo WXiong BLiu WChen CWang SYang C(2024)Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00108(1310-1323)Online publication date: 13-May-2024
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Xiang NYang WRao X(2024)MCoGCN-motif high-order feature-guided embedding learning framework for social link predictionScientific Reports10.1038/s41598-024-80509-914:1Online publication date: 27-Nov-2024
https://doi.org/10.1038/s41598-024-80509-9
Gong JZhao YZhao JZhang JMa GZheng SDu STang J(2024)Personalized recommendation via inductive spatiotemporal graph neural networkPattern Recognition10.1016/j.patcog.2023.109884145:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.109884
Zhang MJiang MTao XWang KKong J(2024)Disentangled representation learning for collaborative filtering based on hyperbolic geometryKnowledge-Based Systems10.1016/j.knosys.2023.111135282:COnline publication date: 27-Feb-2024
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Li KZhang YZhu JLi XWang X(2024)Multi-space interaction learning for disentangled knowledge-aware recommendationExpert Systems with Applications10.1016/j.eswa.2024.124458254(124458)Online publication date: Nov-2024
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