Skip to main content
SpringerLink
Log in

STaTRL: Spatial-temporal and text representation learning for POI recommendation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

With the rapid development of location-based social networks (LBSNs), point-of-interest (POI) recommendations have become a practical problem attracting more and more attention. Recent studies mostly utilize contextual features and sequential patterns of users’ check-ins to recommend POIs. However, there are still many deficiencies in existing works, such as: (1) insufficiently learning relations among far-apart visits in user check-ins; (2) not effectively incorporating geographical information when modeling user-POI interactions; and (3) little exploiting the features from reviews for the POI recommendation task. To tackle the above problems, we propose spatial-temporal and text representation learning (STaTRL), which employs Transformer to learn long-term dependencies among visits in the check-ins sequence and adopts an improved approach to compute the attention between visits by applying geographical information to the self-attention layer in Transformer. Meanwhile, users’ perspectives and POIs’ reputations learned from textual reviews are explored to improve the performance. In addition, a multi-task objective framework is adopted to simultaneously train the hidden representations of users’ historical check-ins trajectories which are shared by these two tasks. Concretely, STaTRL consists of (1) the principal task, i.e., unvisited POI recommendation that recommends to users the unvisited POIs, and (2) the auxiliary task, i.e., user’s POI preference learning whose candidates include both visited and unvisited POIs. We found that the latter task helped train the embedding of visited POIs and further boosted the performance of the former task, and lacking any of both would decline the performance. Extensive experiments on three public datasets demonstrated that STaTRL vastly outperformed the state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://www.yelp.com/dataset

  2. alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools

  3. We downloaded the authors’ source code provided by their URL.

  4. huggingface.co/transformers/pretrained.models.html

  5. https://github.com/pfnet/optuna

  6. huggingface.co/transformers/pretrained.models.html

References

  1. Liu Y, Pham T-AN, Cong G, Yuan Q (2017) An experimental evaluation of point-of-interest recommendation in location-based social networks. In: Proc. of the VLDB Endowment, vol 10, pp 1010–1021

  2. Chen C, Liu Z, Zhao P, Zhou J, Li X (2018) Privacy preserving point-of-interest recommendation using decentralized matrix factorization. In: Proc. of the AAAI Conference on artificial intelligence, vol 32

  3. Rahmani HA, Aliannejadi M, Baratchi M, Crestani F (2020) Joint geographical and temporal modeling base on matrix factorization for point-of-interest recommendation. Advances in Information Retrieval, p 205–219

  4. Zhang Y, Shi Z, Zuo W, Yue L, Liang S, Li X (2020) Joint personalized markov chains with social network embedding for cold-start recommendation. Neurocomputing 386:208–220

    Article  Google Scholar 

  5. Liu S, Wang L (2018) A self-adaptive point-of-interest recommendation algorithm based on a multi-order markov model. Futur Gener Comput Syst 89:506–514

    Article  Google Scholar 

  6. Li G, Chen Q, Zheng B, Yin H, Nguyen QVH, Zhou X (2020) Group-based recurrent neural networks for poi recommendation, ACM/IMS Trans Data Sci 1(1)

  7. Zhao P, Zhu H, Liu Y, Xu J, Li Z, Zhuang F, Sheng VS, Zhou X (2019) Where to go next: A spatio-temporal gated network for next poi recommendation. AAAI, p 5877–5884

  8. Chang B, Park Y, Park D, Kim S, Kang J (2018) Content-aware hierarchical point-of-interest embedding model for successive poi recommendation. IJCAI, p 3301–3307

  9. Wang H, Shen H, Ouyang W, Cheng X (2018) Exploiting poi-specific geographical influence for point-of-interest recommendation. IJCAI, p 3877–3883

  10. Liu W, Wang Z-J, Yao B, Yin J (2019) Geo-alm: Poi recommendation by fusing geographical information and adversarial learning mechanism. IJCAI 7:1807–1813

    Google Scholar 

  11. Yao Z (2018) Exploiting human mobility patterns for point-of-interest recommendation. In: Proc. of the Eleventh ACM International conference on web search and data mining, pp 757–758

  12. Cui Y, Sun H, Zhao Y, Yin H, Zheng K (2021) Sequential-knowledge-aware next poi recommendation: A meta-learning approach. ACM Trans Inf Syst (TOIS) 40(2):1–22

    Article  Google Scholar 

  13. Qian T, Liu B, Viet Q, Nguyen H, Yin H (2019) Spatiotemporal representation learning for translation-based poi recommendation. ACM Trans Inf Syst 37(2):1–24

    Article  Google Scholar 

  14. Lim N, Hooi B, Ng S-K, Wang X, Goh YL, Weng R (2020) Stp-udgat: Spatial-temporal-preference user dimensional graph attention network for next poi recommendation. In: Proc. of the 29TH ACM International Conference on Information and Knowledge Management (CIKM’20), pp 845–855

  15. Gao Q, Trajcevski G, Zhou F, Zhang K, Zhong T, Zhang F (2018) Trajectory-based social circle inference. In: Proc. of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 369–378

  16. Zhou F, Yue X, Trajcevski G, Zhong T, Zhang K (2019) Context-aware variational trajectory encoding and human mobility inference. In: Proc. of World Wide Web Conference, pp 3469–3475

  17. Xing S, Liu F, Wang Q, Zhao X, Li T (2019) Content-aware point-of-interest recommendation based on convolutional neural network. Appl Intell 49(3):858–871

    Article  Google Scholar 

  18. Werneck H, Santos R, Silva N, Pereira Adriano CM, Mourão F, Rocha L (2021) Effective and diverse poi recommendations through complementary diversification models. Expert Syst Appl 175:114775

    Article  Google Scholar 

  19. Han P, Li Z, Liu Y, Zhao P, Li J, Wang H, Shang S (2020) Contextualized point-of-interest recommendation. In: Proc. of the 29th International conference on artificial intelligence, pp 2484–2490

  20. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30

  21. Mowlaei ME, Abadeh MS, Keshavarz H (2020) Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl 148:113234

    Article  Google Scholar 

  22. Peng H, Xu L, Bing L, Huang F, Lu W, Si L (2020) Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In: Proc. of the AAAI Conference on Artificial Intelligence, vol 34, pp 8600–8607

  23. Alqaryouti O, Siyam N, Monem AA, Shaalan K (2020) Aspect-based sentiment analysis using smart government review data. Applied Computing and Informatics

  24. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805

  25. Feng S, Tran LV, Cong G, Chen L, Li J, Li F (2020) Hme: A hyperbolic metric embedding approach for next-poi recommendation. In: Proc. of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1429–1438

  26. Ho NL, Lim KH (2021) User preferential tour recommendation based on poi-embedding methods. In: Proc. of 26th International conference on intelligent user interfaces, pp 46–48

  27. Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proc. of the 26th international conference on world wide web companion, pp 153–162

  28. He J, Qi J, Ramamohanarao K (2019) A joint context-aware embedding for trip recommendations. In: Proc. of 2019 IEEE 35th International Conference on Data Engineering (ICDE), IEEE, pp 292–303

  29. Liao J, Liu T, Yin H, Chen T, Wang J, Wang Y (2021) An integrated model based on deep multimodal and rank learning for point-of-interest recommendation. World Wide Web 24(2):631–655

    Article  Google Scholar 

  30. Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: Proc. of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 335–344

  31. Manotumruksa J, Macdonald C, Ounis I (2018) A contextual attention recurrent architecture for context-aware venue recommendation. In: Proc. of The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp 555–564

  32. Zhong T, Zhang S, Zhou F, Zhang K, Trajcevski G, Wu J (2020) Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23:3125–3151

    Article  Google Scholar 

  33. Kipf T N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proc. of the 5th International conference on learning representations, pp 1–14

  34. Luo Y, Liu Q, Liu Z (2021) Stan: Spatio-temporal attention network for next location recommendation. In: Proc. of the Web Conference 2021, pp 2177–2185

  35. Ma C, Zhang Y, Wang Q, Liu X (2018) Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence. In: Proc. of the 27th ACM International Conference on Information and Knowledge Management, pp 697–706

  36. Zuo S, Jiang H, Li Z, Zhao T, Zha H (2020) Transformer hawkes process. In: Proc. of international conference on machine learning, pp 11692–11702

  37. Lai C-H, Hsu C-Y (2021) Rating prediction based on combination of review mining and user preference analysis. Inf Syst 99:101742

    Article  Google Scholar 

  38. Yu D, Mu Y, Jin Y (2017) Rating prediction using review texts with underlying sentiments. Inf Process Lett 117:10–18

    Article  MathSciNet  Google Scholar 

  39. Cheng Z, Ding Y, Zhu L, Kankanhalli M (2018) Aspect-aware latent factor model: Rating prediction with ratings and reviews. In: Proc. of the 2018 World Wide Web conference, pp 639–648

  40. Shen R-P, Zhang H-R, Yu H, Min F (2019) Sentiment based matrix factorization with reliability for recommendation. Expert Syst Appl 135:249–258

    Article  Google Scholar 

  41. Karimi A, Rossi L, Prati A (2021) Adversarial training for aspect-based sentiment analysis with bert. 2020 25th International Conference on Pattern Recognition (ICPR), 8797–8803, IEEE

  42. Song Y, Wang J, Liang Z, Liu Z, Jiang T (2020) Utilizing bert intermediate layers for aspect based sentiment analysis and natural language inference, arXiv:2002.04815

  43. Xu H, Shu L, Yu PS, Liu B (2020) Understanding pre-trained bert for aspect-based sentiment analysis, arXiv:2011.00169

  44. Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: A lite bert for self-supervised learning of language representations, arXiv:1909.11942

  45. Zhang L, Sun Z, Zhang J, Lei Y, Li C, Wu Z, Kloeden H, Klanner F (2020) An interactive multi-task learning framework for next poi recommendation with uncertain check-ins. CAL 301(985):13954

    Google Scholar 

  46. Halder S, Lim KH, Chan J, Zhang X (2021) Transformer-based multi-task learning for queuing time aware next poi recommendation. In: Proc. of Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 510–523

  47. Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proc. of the 26th ACM SIGKDD International conference on knowledge discovery & data mining, pp 2009–2019

  48. Xia B, Bai Y, Yin J, Li Q, Xu L (2020) Mtpr: A multi-task learning based poi recommendation considering temporal check-ins and geographical locations. Appl Sci 10(19):6664

    Article  Google Scholar 

  49. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proc. of KDD, ACM, pp 1082–1090

  50. Han P, Li Z, Liu Y, Zhao P, Li J, Wang H, Shang S (2020) Contextualized point-of-interest recommendation. In: Proc. of twenty-ninth international joint conference on artificial intelligence and seventeenth pacific rim international conference on artificial intelligence IJCAI-PRICAI-20

  51. Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task4: Aspect-based sentiment analysis. In: Proc. of the 8th international workshop on semantic evaluation, pp 27–35

  52. Rahmani H A, Aliannejadi M, Baratchi M, Crestani F (2020) Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation. In: Proc. of European conference on information retrieval, pp 205–219

  53. Baratchi M, Afsharchi M, Crestani F (2020) Lglmf: Local geographical based logistic matrix factorization model for poi recommendation. In: Proc. of information retrieval technology, vol 12004, p 66

  54. Beltagy I, Peters ME, Cohan A (2020) Longformer: The long-document transformer, arXiv:2004.05150v2

  55. Kitaev N, Kaiser L, Levskaya A (2020) Reformer: The efficient transformer, arXiv:2001.04451

  56. Lu J, Yao J, Zhang J, Zhu X, Xu H, Gao W, Xu C, Xiang T, Zhang L (2021) Soft: Softmax-free transformer with linear complexity. Advances in Neural Information Processing Systems 34

  57. Xiong Y, Zeng Z, Chakraborty R, Tan M, Fung G, Li Y, Singh V (2021) Nyströmformer: A nystöm-based algorithm for approximating self-attention. AAAI 35(16):14138

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Grant-in-aid for JSPS, Grant Number 21K12026, and Suzuki foundation.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Xinfeng Wang, Dongjin Yu & Xiaoxiao Sun

  2. Integrated Graduate School of Medicine, Engineering, Agricultural Sciences, Faculty of Engineering, University of Yamanashi, Kofu, 400-8511, Japan

    Xinfeng Wang

  3. Faculty of Engineering, Graduate Faculty of Interdisciplinary Research, University of Yamanashi, Kofu, 400-8511, Japan

    Fumiyo Fukumoto & Jiyi Li

Authors
  1. Xinfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fumiyo Fukumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiyi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dongjin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoxiao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fumiyo Fukumoto or Dongjin Yu.

Ethics declarations

The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest ; and expert testimony or patent-licensing arrangements, or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Additional information

CRediT authorship contribution statement

Xinfeng Wang: Conception and design, Data collection, Software, Analysis and interpretation of results, Writing- original & editing. Fumiyo Fukumoto: Conception and design, Analysis and interpretation of results, Writing- review draft, Supervision. Jiyi Li: Analysis and interpretation of results, Writing- review draft. Dongjin Yu: Writing- review draft, Supervision. Xiaoxiao Sun: Writing- review draft

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Fukumoto, F., Li, J. et al. STaTRL: Spatial-temporal and text representation learning for POI recommendation. Appl Intell 53, 8286–8301 (2023). https://doi.org/10.1007/s10489-022-03858-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03858-w

Keywords

Search

Navigation