CLLP: Contrastive Learning Framework Based on Latent Preferences for Next POI Recommendation
Pages 1473 - 1482
Abstract
Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the users' next interested POI based on their previous check-in information. Most existing studies view the next POI recommendation as a sequence prediction problem but pay little attention to the fine-grained latent preferences of users, neglecting the diversity of user motivations on visiting the POIs. In this paper, we propose a contrastive learning framework based on latent preferences (CLLP) for next POI recommendation, which models the latent preference distributions of users at each POI and then yield disentangled latent preference representations. Specifically, we leverage the cross-local and global spatio-temporal contexts to learn POI representations for dynamically modeling user preferences. And we design a novel distillation strategy to make full use of the collaborative signals from other users for representation optimization. Then, we disentangle multiple latent preferences in POI representations using predefined preference prototypes, while leveraging preference-level contrastive learning to encourage independence of different latent preferences by improving the quality of latent preference representation space. Meanwhile, we employ a multi-task training strategy to jointly optimize all parameters. Experimental results on two real-world datasets show that CLLP achieves the state-of-the-art performance and significantly outperforms all existing solutions. Further investigations demonstrate the robustness of CLLP against sparse and noisy data.
References
[1]
John S Breese, David Heckerman, and Carl Kadie. 2013. Empirical analysis of predictive algorithms for collaborative filtering. arXiv preprint arXiv:1301.7363 (2013).
[2]
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.
[3]
Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence.
[4]
Chenghua Duan, Wei Fan, Wei Zhou, Hu Liu, and Junhao Wen. 2023. CLSPRec: Contrastive Learning of Long and Short-term Preferences for Next POI Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 473--482.
[5]
Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, and Pengtao Xie. 2020. Cert: Contrastive self-supervised learning for language understanding. arXiv preprint arXiv:2005.12766 (2020).
[6]
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 world wide web conference. 1459--1468.
[7]
Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new poi recommendation. (2015).
[8]
Flavio Figueiredo, Bruno Ribeiro, Jussara M Almeida, and Christos Faloutsos. 2016. Tribeflow: Mining & predicting user trajectories. In Proceedings of the 25th international conference on world wide web. 695--706.
[9]
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021).
[10]
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018).
[11]
Qing Guo, Zhu Sun, Jie Zhang, and Yin-Leng Theng. 2020. An attentional recurrent neural network for personalized next location recommendation. In Proceedings of the AAAI Conference on artificial intelligence, Vol. 34. 83--90.
[12]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017).
[13]
Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2018. Disentangled representation learning for non-parallel text style transfer. arXiv preprint arXiv:1808.04339 (2018).
[14]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[15]
Xuewei Li, Aitong Sun, Mankun Zhao, Jian Yu, Kun Zhu, Di Jin, Mei Yu, and Ruiguo Yu. 2023. Multi-Intention Oriented Contrastive Learning for Sequential Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 411--419.
[16]
Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, and Zi Huang. 2021. Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. arXiv preprint arXiv:2106.15814 (2021).
[17]
Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. 2020. Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2009--2019.
[18]
Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, and Ji-Rong Wen. 2022. Feature-aware diversified re-ranking with disentangled representations for relevant recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3327--3335.
[19]
Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proceedings of the AAAI conference on artificial intelligence, Vol. 30.
[20]
Zhiwei Liu, Yongjun Chen, Jia Li, Philip S Yu, Julian McAuley, and Caiming Xiong. 2021a. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021).
[21]
Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S Yu. 2021b. Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer. In Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval. 1608--1612.
[22]
Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. Stan: Spatio-temporal attention network for next location recommendation. In Proceedings of the web conference 2021. 2177--2185.
[23]
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. Advances in neural information processing systems, Vol. 32 (2019).
[24]
Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020. Disentangled self-supervision in sequential recommenders. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 483--491.
[25]
Xuan Rao, Lisi Chen, Yong Liu, Shuo Shang, Bin Yao, and Peng Han. 2022. Graph-flashback network for next location recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1463--1471.
[26]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811--820.
[27]
Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2020. Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 214--221.
[28]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).
[29]
Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[30]
Shoujin Wang, Liang Hu, Yan Wang, Quan Z Sheng, Mehmet Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence.
[31]
Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning. PMLR, 9929--9939.
[32]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled graph collaborative filtering. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 1001--1010.
[33]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726--735.
[34]
Dingqi Yang, Benjamin Fankhauser, Paolo Rosso, and Philippe Cudre-Mauroux. 2020. Location prediction over sparse user mobility traces using rnns. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. 2184--2190.
[35]
Dingqi Yang, Bingqing Qu, Jie Yang, and Philippe Cudre-Mauroux. 2019. Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach. In The world wide web conference. 2147--2157.
[36]
Song Yang, Jiamou Liu, and Kaiqi Zhao. 2022. GETNext: trajectory flow map enhanced transformer for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on research and development in information retrieval. 1144--1153.
[37]
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems, Vol. 33 (2020), 5812--5823.
[38]
Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, and Xing Xie. 2019. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation. In IJCAI. 4213--4219.
[39]
Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014. Lore: Exploiting sequential influence for location recommendations. In Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems. 103--112.
[40]
Richard Zhang, Phillip Isola, and Alexei A Efros. 2016. Colorful image colorization. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part III 14. Springer, 649--666.
[41]
Pengpeng Zhao, Anjing Luo, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S Sheng, and Xiaofang Zhou. 2020. Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 5 (2020), 2512--2524.
[42]
Tianyu Zhu, Leilei Sun, and Guoqing Chen. 2021. Embedding disentanglement in graph convolutional networks for recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 1 (2021), 431--442. iogr
Index Terms
- CLLP: Contrastive Learning Framework Based on Latent Preferences for Next POI Recommendation
Recommendations
Spatio-Temporal Hypergraph Learning for Next POI Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalNext Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as ...
Adaptive Graph Representation Learning for Next POI Recommendation
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalNext Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from ...
Disentangled Contrastive Hypergraph Learning for Next POI Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalNext point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Most existing sequential-based and graph neural network-based methods have explored various approaches to modeling user ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 July 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
SIGIR 2024
Sponsor:
SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 562Total Downloads
- Downloads (Last 12 months)562
- Downloads (Last 6 weeks)89
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in