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A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN

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Abstract

Point of interest (POI) recommendation problem in location based social network (LBSN) is of great importance and the challenge lies in the data sparsity, implicit user feedback and personalized preference. To improve the precision of recommendation, a tensor decomposition based collaborative filtering (TDCF) algorithm is proposed for POI recommendation. Tensor decomposition algorithm is utilized to fill the missing values in tensor (user-category-time). Specifically, locations are replaced by location categories to reduce dimension in the first phase, which effectively solves the problem of data sparsity. In the second phase, we get the preference rating of users to POIs based on time and user similarity computation and hypertext induced topic search (HITS) algorithm with spatial constraints, respectively. Finally the user’s preference score of locations are determined by two items with different weights, and the Top-N locations are the recommendation results for a user to visit at a given time. Experimental results on two LBSN datasets demonstrate that the proposed model gets much higher precision and recall value than the other three recommendation methods.

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References

  1. Chen B, Yu S, Tang J, He M (2017) Using function approximation for personalized point-of-interest recommendation. Expert Syst Appl 79:225–235

    Article  Google Scholar 

  2. Cheng C, Yang H, King I (2012) Fused matric factorization with geographical and social influence in location based social networks. In: Proceedings of the 26th AAAI conference on Artificial Intelligence. pp 17–23

  3. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: User movement in location-based social networks. In: Proceedings of the ACM International Conference on Knowledge Discovery and DataMining. pp 1082–1090

  4. Gao HJ (2014) Personalized poi recommendation on location-based social networks. Arizona State University, Tempe, USA

    Google Scholar 

  5. Gao R, Li J, Li XF, Song CF, Zhou Y (2018) A personalized point-of-interest recommendation model via fusion of geo-social information. Neurocomputing 273:159–170

    Article  Google Scholar 

  6. Guo L, Jiang H, Liu X, Xing C (2019) Network embedding-aware point-of-interest recommendation in location-based social networks. Complexity 3574194:1–18

    Google Scholar 

  7. Herlocker JL, Konstan J, Terveen L (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  8. Hu B, Ester M (2014) Spatial topic modeling for point of interest recommendation in location based social networks. In: Proceedings of the IEEE International Conference on Data mining. pp 845–850

  9. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM conference on Recommender systems. pp 135–142

  10. Kolda TG, Bader B (2009) Tensor decompositions and applications. SIAM Review 51(3):455–500

    Article  MathSciNet  Google Scholar 

  11. Leung KWT, Lee DL, Lee WC (2011) Fclr: a collaborative location recommendation framework based on co-clustering. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 305–314

  12. Liu B, Xiong H, Papadimitriou S, Fu Y, Yao Z (2015) A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans Knowl Data Eng 27(5):1167–1179

    Article  Google Scholar 

  13. Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: learning potential checkins from friends. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp 975–984

  14. Li X, Jiang M, Hong H (2017) A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization. ACM Trans Inf Syst 35(4):31:1–31:23

  15. Ma H, King I, Lyu M (2009) Learning to recommend with social trust ensemble. In: Proceedings of the Thirty-second international ACM Conference on Research and Development in Information Retrieval. pp 203–210

  16. Park MH, Hong JH, Cho SB (2007) Location based recommendation system using bayesian user’s preference model in mobile devices. In: Proceedings of International Conference on Ubiquitous Intelligence and Computing. pp 1130–1139

  17. Qian X, Feng H, Zhao G, Mei T (2014) Personalized recommendation combing user interest and social circle. IEEE Trans Knowl Data Eng 26(7):1763–1777

    Article  Google Scholar 

  18. Ren X, Song M (2017) Context aware probabilistic matrix factorization modeling for point of interest recommendation. Neurocomputing 241:38–55

    Article  Google Scholar 

  19. Saleem MA, Lee YK, Lee S (2013) Dynamicity in social trends towards trajectory based location recommendation. In: Proceedings of the 11th International Conference on Smart Homes and Health Telematics. pp 86–93

  20. Si YL, Zhang FZ, Liu WY (2017) A time-aware poi recommendation method exploiting user-based collaborative filtering and location popularity. In: 2017 2nd International Conference on Communications, Information Management and Network Security. pp 1–7

  21. Si Y, Zhang F, Liu W (2017) CTF-ARA: An adaptive method for poi recommendation based on check-in and temporal features. Knowl Based Syst 128:59–70

    Article  Google Scholar 

  22. Si YL, Zhang FZ, Liu WY (2019) An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features. Knowl Based Syst 163:267–282

    Article  Google Scholar 

  23. Wang H, Terrovitis M, Mamoulis N (2013) Location recommendation in location-based social networks using user check-in data. In: Proceedings of the Twenty-first ACM international Conference on Advances in Geographic Information Systems. pp 374–383

  24. 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 

  25. Ye M, Janowicz K, Mulligann C (2011) What you are is when you are, the temporal dimension of feature types in location based social networks. In: Proceedings of the ACM International Symposium on Advances in Geographic information system. pp 102–111

  26. Ying Y, Chen L, Chen G (2017) A temporalaware poi recommendation system using context-aware tensor decomposition and weighted hits. Neurocomputing 242:195–205

    Article  Google Scholar 

  27. Yochum P, Chang L, Gu T, Zhu M (2020) Linked open data in location-based recommendation system on tourism domain: a survey. IEEE Access 8:16409–16439

    Article  Google Scholar 

  28. Yuan Q, Cong G, Ma Z, Sun A, Thalmann N (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on Research and Development in Information Retrieval. pp 363–372

  29. Yuan Q, Cong G, Sun A (2014) Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. pp 659–668

  30. Zhao S, Zhao T, Yang H, Lyu MR, King I (2016) Stellar: Spatial-temporal latent ranking for successive point-of-interest recommendation. In: Proceedings of the 30th AAAI International Conference on Artificial Intelligence. pp 315–321

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Acknowledgements

This research is supported by National Natural Science Foundation of China grants 61806083, 61872158; The Fundamental Research Funds for the Central Universities,JLU grants 93K172021Z02; Excellent Young Talents Program for department of Science and Technology of Jilin Province of China grants 20190103051JH, Science and Technology program of the 13th Five-Year Plan for education department of Jilin Province of China grants JJKH20190161KJ.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China

    Minghao Yin, Yanheng Liu & Geng Sun

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China

    Yanheng Liu & Xu Zhou

  3. Center for Computer Fundamental Education, Jilin University, Changchun, Jilin, 130012, China

    Xu Zhou

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  1. Minghao Yin
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  2. Yanheng Liu
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  3. Xu Zhou
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  4. Geng Sun
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Correspondence to Xu Zhou.

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This research is supported by National Natural Science Foundation of China grants 61806083, 61872158; The Fundamental Research Funds for the Central Universities,JLU grants 93K172021Z02; Excellent Young Talents Program for department of Science and Technology of Jilin Province of China grants 20190103051JH, Science and Technology program of the 13th Five-Year Plan for education department of Jilin Province of China grants JJKH20190161KJ.

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Yin, M., Liu, Y., Zhou, X. et al. A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN. Multimed Tools Appl 80, 36215–36235 (2021). https://doi.org/10.1007/s11042-021-11407-9

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  • DOI: https://doi.org/10.1007/s11042-021-11407-9

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