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POI recommendation through cross-region collaborative filtering

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Abstract

Recommending points of interest (POIs) to a user according to the user’s current location and past check-in activities is the focus in this paper. Previously proposed probabilistic and topic model-based methods predict the POIs based on the distribution of the POIs visited in the past, assuming that the next POI for the user follows the same distribution. Such methods tend to recommend the POIs in the cities or regions that the user has visited before because only such cities or regions have observed ratings for the user. Thus, these works are not suitable for a user who travels to a new city or region where she has not checked-in any POI previously. To address this issue, we distinguish the user preferences on the content of POIs from the user preferences on the POIs themselves. The former is long term and is independent of where POIs are located, and the latter is short term and is constrained by the proximity of the location of the POI and the user’s current location. This distinction motivates a location-independent modeling of user’s content preferences of POIs, and a location-aware modeling of user’s location preferences of POIs. The final recommendation of POIs is derived by combining the predicted rating on content and the predicted rating on location of POI. We evaluate this method using the Yelp and Foursquare data sets. This approach has superiority over the state-of-the-art and works well in the “new city” situation in which the user has not rated any of the POIs in the current region.

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Notes

  1. http://www.yelp.com/dataset_challenge/.

  2. https://foursquare.com/.

References

  1. Adrienko N, Adrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2):205–219

    Article  Google Scholar 

  2. Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: GIS, pp 199–208

  3. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI, vol 12. pp 17–23

  4. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: IJCAI, AAAI Press, pp 2605–2611

  5. Cheng Z, Caverlee J, Kamath KY, Lee K (2011) Toward traffic-driven location-based web search. In: CIKM, pp 805–814

  6. Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: RecSys

  7. Fuchs M, Zanker M (2012) Multi-criteria ratings for recommender systems: an empirical analysis in the tourism domain. Springer, Berlin

    Google Scholar 

  8. Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. In: RecSys, pp 25–32

  9. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 168–177

  10. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

  11. 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: WSDM, pp 375–384

  12. Leung KW-T, Lee DL, Lee W-C (2011) Clr: a collaborative location recommendation framework based on co-clustering. In: SIGIR, pp 305–314

  13. Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) Lars: a location-aware recommender system. In: ICDE, IEEE, pp 450–461

  14. Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: KDD, pp 1043–1051

  15. Rösler R, Liebig T (2013) Using data from location based social networks for urban activity clustering. In: Geographic information science at the heart of Europe, Springer, pp 55–72

  16. Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. In: NIPS, pp 1257–1264

  17. Sang J, Mei T, Sun J-T, Xu C, Li S (2012) Probabilistic sequential POIs recommendation via check-in data. In: GIS, pp 402–405

  18. Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46:234–240

  19. Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: KDD, pp 448–456

  20. Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: GIS, pp 458–461

  21. Ye M, Yin P, Lee W-C, Lee D-L (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp 325–334

  22. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) Lcars: a location-content-aware recommender system. In: KDD, pp 221–229

  23. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. In: KDD, pp 186–194

  24. Zhang C, Wang K, Lim E-p, Xu Q, Sun J, Yu H (2015) Are features equally representative? a feature-centric recommendation. In: AAAI

  25. Zhang C, Wang K, Yu H, Sun J, Lim E-p (2014) Latent factor transition for dynamic collaborative filtering. In: SDM, pp 452–460

  26. Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with gps history data. In: WWW, pp 1029–1038

  27. Zheng Y, Zhang L, Ma Z, Xie X, Ma W-Y (2011) Recommending friends and locations based on individual location history. ACM Trans Web 5(1):1–44

    Article  MATH  Google Scholar 

  28. Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp 791–800

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Acknowledgments

We thank anonymous reviewers for their valuable feedback. This work is supported by a Discovery Grant from Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, 38 Zheda Road, Hangzhou, China

    Chenyi Zhang

  2. School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, Canada

    Chenyi Zhang & Ke Wang

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  1. Chenyi Zhang
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  2. Ke Wang
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Correspondence to Chenyi Zhang.

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Zhang, C., Wang, K. POI recommendation through cross-region collaborative filtering. Knowl Inf Syst 46, 369–387 (2016). https://doi.org/10.1007/s10115-015-0825-8

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