research-article

City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model

Authors:

Tieyun QianAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 6

Article No.: 153, Pages 1 - 27

https://doi.org/10.1145/3664284

Published: 19 August 2024 Publication History

Abstract

Existing sequential Point of Interest (POI) recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a Dual-Target Cross-City Sequential POI Recommendation model (DCSPR) to achieve the purpose of complementary learning across cities. On one hand, DCSPR respectively captures geographical and cultural characteristics for each city by mining intra-city regions and intra-city functions of POIs. On the other hand, DCSPR builds a transfer channel between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation, DCSPR involves a new region- and function-aware network for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of DCSPR.

References

[1]

Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, and Bin Wang. 2022. Contrastive cross-domain sequential recommendation. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 138–147.

Digital Library

[2]

Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. 2021. Curriculum meta-learning for next POI recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2692–2702.

Digital Library

[3]

Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the 23th International Joint Conference on Artificial Intelligence.

[4]

Qiang Cui, Chenrui Zhang, Yafeng Zhang, Jinpeng Wang, and Mingchen Cai. 2021. ST-PIL: Spatial-temporal periodic interest learning for next point-of-interest recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2960–2964.

Digital Library

[5]

Zhi-Hong Deng, Chang-Dong Wang, Ling Huang, Jian-Huang Lai, and S. Yu Philip. 2022. G\({\hat{ }}\) 3SR: Global graph guided Session-based recommendation. IEEE Transactions on Neural Networks and Learning Systems 34, 12 (2022), 9671–9684.

[6]

Jingtao Ding, Guanghui Yu, Yong Li, Depeng Jin, and Hui Gao. 2019. Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2019), 1–28.

Digital Library

[7]

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 27th International Conference on World Wide Web. 1459–1468.

Digital Library

[8]

Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, and Yeow Meng Chee. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the 24th International Conference on Artificial Intelligence. 2069–2075.

Digital Library

[9]

Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc V. H. Nguyen, and Hongzhi Yin. 2021. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2483–2489.

[10]

Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Kaixin Gao, Bing Han, Lin Zheng, Sheng Guo, and Xiaobo Guo. 2023. Poincaré heterogeneous graph neural networks for sequential recommendation. ACM Transactions on Information Systems 41, 3 (2023), 1–26.

Digital Library

[11]

Jing He, Xin Li, Lejian Liao, Dandan Song, and William Cheung. 2016. Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 137–143.

[12]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639–648.

Digital Library

[13]

Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, and Tao Mei. 2015. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia 17, 6 (2015), 907–918.

Digital Library

[14]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE 18th International Conference on Data Mining. 197–206.

[15]

Dejiang Kong and Fei Wu. 2018. HST-LSTM: A hierarchical spatial-temporal long-short term memory network for location prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2341–2347.

[16]

Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, and Di Niu. 2022. RecGURU: Adversarial learning of generalized user representations for cross-domain recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 571–581.

Digital Library

[17]

Dichao Li and Zhiguo Gong. 2020. A deep neural network for crossing-city POI recommendations. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3536–3548.

[18]

Pan Li and Alexander Tuzhilin. 2020. DTCDR: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331–339.

Digital Library

[19]

Ranzhen Li, Yanyan Shen, and Yanmin Zhu. 2018. Next point-of-interest recommendation with temporal and multi-level context attention. In Proceedings of the 2018 IEEE 18th International Conference on Data Mining. 1110–1115.

[20]

Yang Li, Tong Chen, Hongzhi Yin, and Zi Huang. 2021. Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. 1491–1497.

[21]

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.

Digital Library

[22]

Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 831–840.

Digital Library

[23]

Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng, and Rui Tan. 2022. Hierarchical multi-task graph recurrent network for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1133–1143.

Digital Library

[24]

Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, and Jagannadan Varadarajan. 2020. STP-UDGAT: Spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 845–854.

Digital Library

[25]

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 30th AAAI Conference on Artificial Intelligence. 194–200.

[26]

Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. 739–748.

Digital Library

[27]

Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-temporal attention network for next location recommendation. In Proceedings of the 30th International Conference on World Wide Web. 2177–2185.

Digital Library

[28]

Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Peiyu Liu, Jun Ma, and Maarten de Rijke. 2022. Mixed information flow for cross-domain sequential recommendations. ACM Transactions on Knowledge Discovery from Data 16, 4 (2022), 1–32.

Digital Library

[29]

Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. \(\pi\)-net: A parallel information-sharing network for shared-account cross-domain sequential recommendations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 685–694.

Digital Library

[30]

Sinno J. Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345–1359.

Digital Library

[31]

Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, and Ming Zhang. 2023. DisenPOI: Disentangling sequential and geographical influence for point-of-interest Recommendation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 508–516.

Digital Library

[32]

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.

Digital Library

[33]

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 34th AAAI Conference on Artificial Intelligence. 214–221.

[34]

Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten De Rijke. 2021. Parallel split-join networks for shared account cross-domain sequential Recommendations. IEEE Transactions on Knowledge and Data Engineering 35, 4 (2021), 4106–4123.

Digital Library

[35]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998–6008.

[36]

En Wang, Yiheng Jiang, Yuanbo Xu, Liang Wang, and Yongjian Yang. 2022a. Spatial-temporal interval aware sequential POI recommendation. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering. IEEE, 2086–2098.

[37]

En Wang, Yuanbo Xu, Yongjian Yang, Fukang Yang, Chunyu Liu, and Yiheng Jiang. 2021. ToP: Time-dependent zone-enhanced points-of-interest embedding-based explainable recommender system. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications. IEEE, 1–10.

Digital Library

[38]

Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, and Dongjin Yu. 2023. EEDN: Enhanced encoder-decoder network with local and global context learning for POI recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 383–392.

Digital Library

[39]

Xiaolin Wang, Guohao Sun, Xiu Fang, Jian Yang, and Shoujin Wang. 2022. Modeling spatio-temporal neighbourhood for personalized point-of-interest recommendation. In Proceedings of the 31st International Joint Conference on Artificial Intelligence. 3530–3536.

[40]

Zhaobo Wang, Yanmin Zhu, Haobing Liu, and Chunyang Wang. 2022. Learning graph-based disentangled representations for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1154–1163.

Digital Library

[41]

Zhaobo Wang, Yanmin Zhu, Chunyang Wang, Wenze Ma, Bo Li, and Jiadi Yu. 2023. Adaptive graph representation learning for next POI recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 393–402.

Digital Library

[42]

Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, and Wei Chu. 2023. Spatio-temporal hypergraph learning for next POI recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 403–412.

Digital Library

[43]

Dingqi Yang, Daqing Zhang, and Bingqing Qu. 2016. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST) 7, 3 (2016), 1–23.

Digital Library

[44]

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.

Digital Library

[45]

Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 325–334.

Digital Library

[46]

Feiyu Yin, Yong Liu, Zhiqi Shen, Lisi Chen, Shuo Shang, and Peng Han. 2023. Next POI recommendation with dynamic graph and explicit dependency. In Proceedings of the 37th AAAI Conference on Artificial Intelligence. 4827–4834.

Digital Library

[47]

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.

Digital Library

[48]

Lu Zhang, Zhu Sun, Ziqing Wu, Jie Zhang, Y Ong, and Xinghua Qu. 2022. Next point-of-interest recommendation with inferring multi-step future preferences. In Proceedings of the 31st International Joint Conference on Artificial Intelligence. 3751–3757.

[49]

Mingwei Zhang, Yang Yang, Rizwan Abbas, Ke Deng, Jianxin Li, and Bin Zhang. 2021. SNPR: A serendipity-oriented next POI recommendation model. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2568–2577.

Digital Library

[50]

Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. 2019. Where to go next: A spatio-temporal gated network for next POI recommendation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 5877–5884.

Digital Library

[51]

Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael Lyu, and Irwin King. 2016. STELLAR: Spatial-temporal latent ranking for successive point-of-interest recommendation. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 315–322.

[52]

Xiaolin Zheng, Jiajie Su, Weiming Liu, and Chaochao Chen. 2022. DDGHM: Dual dynamic graph with hybrid metric training for cross-domain sequential recommendation. In Proceedings of the 30th ACM International Conference on Multimedia. 471–481.

Digital Library

[53]

Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. DTCDR: A framework for dual-target cross-domain recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1533–1542.

Digital Library

[54]

Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A graphical and attentional framework for dual-target cross-domain recommendation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. 3001–3008.

[55]

Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, and Guanfeng Liu. 2023. A unified framework for cross-domain and cross-system recommendations. IEEE Transactions on Knowledge and Data Engineering 35, 2 (2023), 1171–1184.

Cited By

Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893

Index Terms

City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 6

November 2024

813 pages

EISSN:1558-2868

DOI:10.1145/3618085

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 19 August 2024

Online AM: 10 May 2024

Accepted: 29 April 2024

Revised: 24 April 2024

Received: 31 August 2023

Published in TOIS Volume 42, Issue 6

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Cited By

Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893

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