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Integrating geographical and temporal influences into location recommendation: a method based on check-ins

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Abstract

In the online-to-offline (O2O) business model, location recommendation plays an important role and is an essential component of the location-based services. The check-in data, which contains both the geographical and temporal information, has been treated as an important data source for location recommendation. Location-based collaborative filtering is a popular technique for computing location similarities to arrive at the recommendation. In this research we analyze the geographical and temporal characteristics of the user’s check-in activity and incorporate it for deriving recommendations using location-based collaborative filtering. To model the geographical proximity between the recommended location and the visited location, we first get the user’s active regions using the multiple-center discovering algorithm; we then derive the probability of visiting the unvisited locations by using the power-law distribution on the distance. The geographical proximity is derived by multiplying the visiting probability and the check-in ratio of the active region. To consider temporal information, we propose the concept of time-aware location similarity, which splits the user check-ins into twenty-four different time slots in a day. To address the sparsity problem created by splitting check-in data, we propose a mechanism to measure the similarities between time slots and use these similarities to infer the empty ratings. The geographical proximity and time-aware location similarity are integrated to generate the location similarity. We perform the experiments to verify the effectiveness of the proposed algorithm. The experimental results show the superiority of our method compared with the benchmarks.

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Notes

  1. Multiplication is the simplest way to combine the geographical proximity and time-aware similarities to get the final local similarity. In fact, there are many other ways. An integrated way is to use polynomial interpolation to weight the two similarities to compute the final similarity score. However, it will add some additional parameters that require tuning. So, we just use the simplest way if it is enough to prove the efficiency of the proposed recommendation framework.

References

  1. Symeonidis P, Ntempos D, Manolopoulos Y (2014) Location-based social networks. In: Recommender systems for location-based social networks. Springer, Berlin, pp 35–48

  2. Chorley MJ, Whitaker RM, Allen SM (2015) Personality and location-based social networks. Comput Hum Behav 46:45–56

    Article  Google Scholar 

  3. Xiao S, Dong M (2015) Hidden semi-Markov model-based reputation management system for online to offline (O2O) e-commerce markets. Decis Support Syst 77:87–99

    Article  Google Scholar 

  4. Zheng Y (2012) Tutorial on location-based social networks. In: Proceedings of international conference on world wide web, WWW

  5. Gao H, Tang J, Hu X, Liu H (2013) Modeling temporal effects of human mobile behavior on location-based social networks. In: Proceedings of the 22nd ACM international conference on Conference on information and knowledge management, ACM, pp 1673–1678

  6. Preoţiuc-Pietro D, Cohn T (2013) Mining user behaviours: a study of check-in patterns in location based social networks. In: Proceedings of the 5th annual ACM web science conference, ACM, pp 306–315

  7. Yuan H, Qian Y, Yang R, Ren M (2014) Human mobility discovering and movement intention detection with GPS trajectories. Decis Support Syst 63:39–51

    Article  Google Scholar 

  8. Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web, ACM, pp 791–800

  9. Wiese J, Kelley PG, Cranor LF, Dabbish L, Hong JI, Zimmerman J (2011) Are you close with me? are you nearby? Investigating social groups, closeness, and willingness to share. In: Proceedings of the 13th international conference on ubiquitous computing, ACM, pp 197–206

  10. Hasan S, Zhan X, Ukkusuri SV (2013) Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, ACM, p 6

  11. Ye J, Zhu Z, Cheng H (2013) What’s your next move: user activity prediction in location-based social networks. In: Proceedings of the SIAM international conference on data mining, SIAM, pp 171–179

  12. Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, ACM, pp 458–461

  13. Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. arXiv preprint arXiv:160700647

  14. Ho SY (2012) The effects of location personalization on individuals’ intention to use mobile services. Decis Support Syst 53(4):802–812

    Article  Google Scholar 

  15. Wang H, Terrovitis M, Mamoulis N (2013) Location recommendation in location-based social networks using user check-in data. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, ACM, pp 374–383

  16. Bao J, Zheng Y, Wilkie D, Mokbel M (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19(3):525–565

    Article  Google Scholar 

  17. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  18. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1–19

    Article  Google Scholar 

  19. Herlocker J, Konstan JA, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5(4):287–310

    Article  Google Scholar 

  20. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI conference on artificial intelligence, AAAI, pp 17–23

  21. Ye M, Yin P, Lee W-C, Lee D-L (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, ACM, pp 325–334

  22. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 363–372

  23. 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, ACM, pp 93–100

  24. Zhang J-D, Chow C-Y (2013) iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, ACM, pp 334–343

  25. Park M-H, Hong J-H, Cho S-B (2007) Location-based recommendation system using bayesian user’s preference model in mobile devices. In: Indulska J, Ma J, Yang LT, Ungerer T, Cao J (eds) Ubiquitous intelligence and computing. Springer, Berlin, pp 1130–1139

    Chapter  Google Scholar 

  26. Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM conference on recommender systems, ACM, pp 25–32

  27. Kurashima T, Iwata T, Hoshide T, Takaya N, Fujimura K (2013) Geo topic model: joint modeling of user’s activity area and interests for location recommendation. In: Proceedings of the sixth ACM international conference on web search and data mining, ACM, pp 375–384

  28. Leung KW-T, Lee DL, Lee W-C (2011) CLR: 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, ACM, pp 305–314

  29. Ference G, Ye M, Lee W-C (2013) Location recommendation for out-of-town users in location-based social networks. In: Proceedings of the 22nd ACM international conference on conference on information and knowledge management, ACM, pp 721–726

  30. Tang J, Gao H, Liu H, Das Sarma A (2012) eTrust: understanding trust evolution in an online world. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 253–261

  31. Gao H, Tang J, Liu (2012) H Mobile location prediction in spatio-temporal context. In: Nokia mobile data challenge workshop, p 44

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Acknowledgements

The funding was supported by National Natural Science Foundation of China (Grant Nos. 71731005, 71331002, 71571059), National Key R&D Program of China (Grant No. 2017YFC0820106).

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Authors and Affiliations

  1. National Engineering Lab for Public Security Risk Perception and Control by Big Data (PSRPC), China, Academy of Electronics and Information Technology, Beijing, 100041, People’s Republic of China

    Rui Duan

  2. School of Management, Hefei University of Technology, No. 193, Tunxi Road, Hefei, 230009, Anhui, People’s Republic of China

    Rui Duan, Cuiqing Jiang, Yong Ding & Deyou Shu

  3. College of Business, University of Tennessee–Chattanooga, Tennessee, TN, 37403, USA

    Hemant K. Jain

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  1. Rui Duan
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Correspondence to Cuiqing Jiang.

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Duan, R., Jiang, C., Jain, H.K. et al. Integrating geographical and temporal influences into location recommendation: a method based on check-ins. Inf Technol Manag 20, 73–90 (2019). https://doi.org/10.1007/s10799-018-0293-4

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