research-article

Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation

Authors:

Fanyu KongAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 372 - 382

https://doi.org/10.1145/3539618.3591730

Published: 18 July 2023 Publication History

Abstract

As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of recommendation tasks. Thus many works consider obtaining user and item representations via information aggregation on graph-structured data within Euclidean space. However, the scale-free graphs (e.g., KGs) inherently exhibit non-Euclidean geometric topologies, such as tree-like and circle-like structures. The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. On the one hand, it aims to preserve various global geometric structures in KG with mixed-curvature manifold spaces as the backbone. On the other hand, we integrate Ricci curvature into graph convolutional networks (GCNs) to capture local geometric structural properties when aggregating neighbor nodes. Besides, to exploit the expressive spatial features in KG, we incorporate interaction learning to ensure the geometric message passing between curved manifolds. Specifically, we adopt curvature-aware geodesic distance metrics to maximize the mutual information between Euclidean space and non-Euclidean spaces. Through extensive experiments, we demonstrate that the proposed CurvRec outperforms state-of-the-art baselines.

References

[1]

Gregor Bachmann, Gary Bécigneul, and Octavian Ganea. 2020. Constant curvature graph convolutional networks. In International Conference on Machine Learning. PMLR, 486--496.

[2]

James W Cannon, William J Floyd, Richard Kenyon, Walter R Parry, et al. 1997. Hyperbolic geometry. Flavors of geometry, Vol. 31, 59--115 (1997), 2.

[3]

Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, and Jiao Xu. 2021. Social-Enhanced Attentive Group Recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 3 (2021), 1195--1209.

[4]

Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. 2022. Geometry Interaction Knowledge Graph Embeddings. In AAAI Conference on Artificial Intelligence.

[5]

Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, Vol. 32 (2019).

[6]

Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, and Irwin King. 2022. Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 94--102.

Digital Library

[7]

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).

[8]

Sanjeev Dhawan, Kulvinder Singh, Adrian Rabaea, and Amit Batra. 2022. ImprovedGCN: An Efficient and Accurate Recommendation System employing lightweight Graph Convolutional Networks in Social Media. Electronic Commerce Research and Applications (2022), 101191.

[9]

Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, and Yunjun Gao. 2022. MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).

[10]

Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, and Ivan Oseledets. 2022. Hyperbolic Vision Transformers: Combining Improvements in Metric Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7409--7419.

[11]

Mingxin Gan and Yingxue Ma. 2023. Mapping user interest into hyper-spherical space: A novel POI recommendation method. Information Processing & Management, Vol. 60, 2 (2023), 103169.

Digital Library

[12]

Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018. Hyperbolic neural networks. Advances in neural information processing systems, Vol. 31 (2018).

[13]

Albert Gu, Frederic Sala, Beliz Gunel, and Christopher Ré. 2019. Learning mixed-curvature representations in product spaces. In International Conference on Learning Representations.

[14]

Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, et al. 2018. Hyperbolic attention networks. arXiv preprint arXiv:1805.09786 (2018).

[15]

Junyu Hao and Xiaoge Li. 2022. User Information Enhanced Knowledge Graph Convolutional Networks for Recommender Systems. In 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 1232--1237.

[16]

Jürgen Jost and Shiping Liu. 2014. Ollivier's Ricci curvature, local clustering and curvature-dimension inequalities on graphs. Discrete & Computational Geometry, Vol. 51, 2 (2014), 300--322.

Digital Library

[17]

Diederik P. Kingma and Jimmy Lei Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR 2015: International Conference on Learning Representations 2015.

[18]

Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster).

[19]

Jianxin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, and Hao Peng. 2022. Curvature Graph Generative Adversarial Networks. In Proceedings of the ACM Web Conference 2022. 1528--1537.

Digital Library

[20]

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.

[21]

Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao. 2019. DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 344--352.

Digital Library

[22]

Han Liu, Yinwei Wei, Jianhua Yin, and Liqiang Nie. 2022. Hs-gcn: Hamming spatial graph convolutional networks for recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).

[23]

Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, and Le Song. 2017. Deep hyperspherical learning. Advances in neural information processing systems, Vol. 30 (2017).

[24]

Ting Ma, Longtao Huang, Qianqian Lu, and Songlin Hu. 2022. KR-GCN: Knowledge-aware Reasoning with Graph Convolution Network for Explainable Recommendation. ACM Transactions on Information Systems (TOIS) (2022).

[25]

Maximillian Nickel and Douwe Kiela. 2017. Poincaré embeddings for learning hierarchical representations. Advances in neural information processing systems, Vol. 30 (2017).

[26]

Maximillian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In International Conference on Machine Learning. PMLR, 3779--3788.

[27]

Yann Ollivier. 2009. Ricci curvature of Markov chains on metric spaces. Journal of Functional Analysis, Vol. 256, 3 (2009), 810--864.

[28]

Ludger Rüschendorf. 1985. The Wasserstein distance and approximation theorems. Probability Theory and Related Fields, Vol. 70, 1 (1985), 117--129.

[29]

Xiao Sha, Zhu Sun, and Jie Zhang. 2019. Attentive Knowledge Graph Embedding for Personalized Recommendation. arXiv preprint arXiv:1910.08288 (2019).

[30]

Jayson Sia, Edmond Jonckheere, and Paul Bogdan. 2019. Ollivier-ricci curvature-based method to community detection in complex networks. Scientific reports, Vol. 9, 1 (2019), 9800.

[31]

Ondrej Skopek, Octavian-Eugen Ganea, and Gary Bécigneul. 2020. Mixed-curvature variational autoencoders. In 8th International Conference on Learning Representations (ICLR 2020)(virtual). International Conference on Learning Representations.

[32]

Mingyang Song, Yi Feng, and Liping Jing. 2022. Hyperbolic Relevance Matching for Neural Keyphrase Extraction. arXiv preprint arXiv:2205.02047 (2022).

[33]

Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, and Maksims Volkovs. 2021. Hgcf: Hyperbolic graph convolution networks for collaborative filtering. In Proceedings of the Web Conference 2021. 593--601.

Digital Library

[34]

Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, and Lun-Wei Ku. 2020. MVIN: Learning Multiview Items for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 99--108.

Digital Library

[35]

Shaohua Tao, Runhe Qiu, Bo Xu, and Yuan Ping. 2022. Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation. Knowledge-Based Systems, Vol. 251 (2022), 109300.

Digital Library

[36]

Hao Wang, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, and Enhong Chen. 2021c. Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Transactions on Information Systems (TOIS), Vol. 40, 2 (2021), 1--28.

Digital Library

[37]

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 417--426.

Digital Library

[38]

Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019b. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 968--977.

Digital Library

[39]

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019c. Knowledge Graph Convolutional Networks for Recommender Systems. In The World Wide Web Conference. 3307--3313.

[40]

Liping Wang, Fenyu Hu, Shu Wu, and Liang Wang. 2021a. Fully hyperbolic graph convolution network for recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3483--3487.

Digital Library

[41]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019a. KGAT: Knowledge Graph Attention Network for Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 950--958.

Digital Library

[42]

Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. 2021b. Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the Web Conference 2021. 878--887.

Digital Library

[43]

Xiting Wang, Kunpeng Liu, Dongjie Wang, Le Wu, Yanjie Fu, and Xing Xie. 2022. Multi-level recommendation reasoning over knowledge graphs with reinforcement learning. In Proceedings of the ACM Web Conference 2022. 2098--2108.

Digital Library

[44]

Xing Wu, Yisong Li, Jianjia Wang, Quan Qian, and Yike Guo. 2022. UBAR: User Behavior-Aware Recommendation with knowledge graph. Knowledge-Based Systems (2022), 109661.

[45]

Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019. Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 125--134.

Digital Library

[46]

Peng Yang, Chengming Ai, Yu Yao, and Bing Li. 2022a. EKPN: enhanced knowledge-aware path network for recommendation. Applied Intelligence, Vol. 52, 8 (2022), 9308--9319.

Digital Library

[47]

Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022b. Knowledge Graph Contrastive Learning for Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1434--1443.

Digital Library

[48]

Yun Yang, Yulong Rao, Minghao Yu, and Yan Kang. 2022c. Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation. Neural Networks, Vol. 146 (2022), 1--10.

Digital Library

[49]

Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, and Hongxia Yang. 2019. Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 679--688.

Digital Library

[50]

Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, and Jiawei Han. 2013. Recommendation in heterogeneous information networks with implicit user feedback. In Proceedings of the 7th ACM conference on Recommender systems. 347--350.

Digital Library

[51]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353--362.

Digital Library

[52]

Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng, and Qi Zhang. 2022. Geometric Disentangled Collaborative Filtering. In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. ACM, 80--90.

[53]

Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, and Dik Lun Lee. 2017. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 635--644.

Digital Library

[54]

Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, and Xiaofei He. 2019. IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2347--2357.

Digital Library

[55]

Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, and Haiyong Xie. 2022. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems, Vol. 41, 2 (2022), 1--26.

Digital Library

[56]

Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. 2020. Graph geometry interaction learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7548--7558.

Cited By

Zhang LShi YQi KWu DWang XYan ZChen Z(2025)Cross-space topological contrastive learning for knowledge graph-aware issue recommendationKnowledge and Information Systems10.1007/s10115-025-02355-zOnline publication date: 18-Feb-2025
https://doi.org/10.1007/s10115-025-02355-z
Yuan XChen JWang YChen AHuang YZhao WYu S(2024)Semantic-Enhanced Knowledge Graph CompletionMathematics10.3390/math1203045012:3(450)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030450
Li KZhang YZhu JLi XWang X(2024)Multi-space interaction learning for disentangled knowledge-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124458254:COnline publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124458

Index Terms

Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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Key Research and Development Plan of Shandong Province (Major Scientific and Technological Innovation Project)
RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund, Singapore
National Research Foundation, Singapore and DSO National Laboratories under the AI Singapore Programme

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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

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Cited By

Zhang LShi YQi KWu DWang XYan ZChen Z(2025)Cross-space topological contrastive learning for knowledge graph-aware issue recommendationKnowledge and Information Systems10.1007/s10115-025-02355-zOnline publication date: 18-Feb-2025
https://doi.org/10.1007/s10115-025-02355-z
Yuan XChen JWang YChen AHuang YZhao WYu S(2024)Semantic-Enhanced Knowledge Graph CompletionMathematics10.3390/math1203045012:3(450)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030450
Li KZhang YZhu JLi XWang X(2024)Multi-space interaction learning for disentangled knowledge-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124458254:COnline publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124458

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