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A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction

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Abstract

Point of interest (POI) recommendation is a fundamental task in location-based social networks (LBSN). The increasing proliferation of LBSNs brings about considerable amounts of user-generated check-in data. Such data can significantly contribute to understanding user behaviors, based on which personalized recommendations can be efficiently derived. Spatial and temporal effects are crucial factors in the user’s decision-making for choosing a POI to visit. Most existing methods treat them as two independent features and cannot accurately capture users’ interests. We argue that spatial and temporal effects should be analyzed simultaneously in POI recommendations. To this end, we propose a S patioT emporal heterogeneous information Network (HIN)-based PO I RE commendation model (STORE) to model various heterogeneous context features, e.g., the joint spatiotemporal effects, types of POI, and social relations. Specifically, we defined the spatiotemporal effects entity (St) in HIN to model the joint spatiotemporal effects. Instead of modeling the traditional two-way interaction <user, item>, we further design a four-way neural interaction model <User, Meta-path, St, POI>. In this way, our model can effectively mine and extract useful information from the meta-path-based context and spatiotemporal effects, thereby improving recommendation performance. We conduct extensive experiments on two real-world datasets, and the results demonstrate that the STORE model outperforms the best baseline by about 12% in NDCG@5 and 11% in Rec@5.

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Notes

  1. https://foursquare.com/

  2. https://www.yelp.com/

  3. https://sites.google.com/site/dbhongzhi

References

  1. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-based Syst 46:109–132

    Article  Google Scholar 

  2. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 191–198

  3. Fu Ty, Lee WC, Lei Z (2017) Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1797–1806

  4. Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on Recommender systems, pp 93–100

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

  6. Griesner JB, Abdessalem T, Naacke H (2015) Poi recommendation: Towards fused matrix factorization with geographical and temporal influences. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp 301–304

  7. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864

  8. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182

  9. Holzinger K, Lehner M, Fassold M, Holzinger A (2011) Archaeological scavenger hunt on mobile devices: From e-education to e-business: a triple adaptive mobile application for supporting experts, tourists and children. In: Proceedings of the International Conference on e-Business, pp 1–6. IEEE

  10. Hu B, Shi C, Zhao WX, Yu PS (2018) Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1531–1540

  11. Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: Learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 975–984

  12. Li X, Cong G, Li X, Pham TAN, Krishnaswamy S (2015) Rank-geofm: a ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 433–442

  13. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (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, pp 831–840

  14. Liu J, Shi C, Hu B, Liu S, Philip SY (2017) Personalized ranking recommendation via integrating multiple feedbacks Pacific-asia conference on knowledge discovery and data mining. Springer, pp 131–143

  15. Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp 739–748

  16. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  17. Manotumruksa J, Macdonald C, Ounis I (2017) A deep recurrent collaborative filtering framework for venue recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1429–1438

  18. Manotumruksa J, Macdonald C, Ounis I (2018) A contextual attention recurrent architecture for context-aware venue recommendation. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 555– 564

  19. Niemeyer G Geohash. https://en.wikipedia.org/wiki/Geohash#cite_note-17/. Accessed September 6, 2020

  20. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710

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

    Article  Google Scholar 

  22. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

  23. Scellato S, Noulas A, Lambiotte R, Mascolo C (2011) Socio-spatial properties of online location-based social networks. In: Fifth international AAAI conference on weblogs and social media

  24. Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37

    Article  Google Scholar 

  25. Su Y, Li X, Zha D, Tang W, Jiang Y, Xiang J, Gao N (2019) Hrec: Heterogeneous graph embedding-based personalized point-of-interest recommendation. In: International conference on neural information processing. Springer, pp 37–49

  26. Sun Y, Han J, Yan X, Yu P.S., Wu T (2011) Pathsim: Meta pathbased topk similarity search in heterogeneous information networks. Very Large Data Bases 4(11):992–1003

    Google Scholar 

  27. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077

  28. Tran TNT, Felfernig A, Trattner C, Holzinger A (2020) Recommender systems in the healthcare domain: state-of-the-art and research issues. J Intell Inf Syst:1–31

  29. Wang H, Fu Y, Wang Q, Yin H, Du C, Xiong H (2017) A location-sentiment-aware recommender system for both home-town and out-of-town users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1135–1143

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

  31. Wang J, Huang P, Zhao H, Zhang Z, Zhao B, Lee DL (2018) Billion-scale commodity embedding for e-commerce recommendation in alibaba. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 839–848

  32. Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X (2017) St-sage: a spatial-temporal sparse additive generative model for spatial item recommendation. ACM Trans Intell Syst Technol (TIST) 8 (3):1–25

    Article  Google Scholar 

  33. Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp 15–24

  34. Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans Syst Man Cybern Syst 45(1):129– 142

    Article  Google Scholar 

  35. Yu F, Cui L, Guo W, Lu X, Li Q, Lu H (2020) A category-aware deep model for successive poi recommendation on sparse check-in data. In: Proceedings of The Web Conference 2020, pp 1264– 1274

  36. Yu X, Ren X, Gu Q, Sun Y, Han J (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks. IJCAI HINA:27

  37. Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp 283–292

  38. Zhao H, Yao Q, Li J, Song Y, Lee DL (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, pp 635–644

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

  40. Zhao S, Zhao T, Yang H, Lyu MR, King I (2016) Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Thirtieth AAAI conference on artificial intelligence

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Acknowledgments

This work was supported by the National Natural Science of Foundation of China (No.61902010, 61671030), Beijing Excellent Talent Funding-Youth Project (No. 2018000020124G039), and the Project of Beijing Municipal Education Commission (No.KM202110005025).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Yongheng Liu, Zhen Yang, Tong Li & Di Wu

  2. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, China

    Zhen Yang & Tong Li

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  1. Yongheng Liu
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  2. Zhen Yang
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  3. Tong Li
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  4. Di Wu
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Correspondence to Tong Li.

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Liu, Y., Yang, Z., Li, T. et al. A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction. Appl Intell 52, 5310–5324 (2022). https://doi.org/10.1007/s10489-021-02677-9

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