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Recommending friends and locations based on individual location history

Published:17 February 2011Publication History
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Abstract

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.

References

  1. Adomavicius, G. and Tuzhhilin, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Engin. 17, 6, 734--749. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ankerst, M., Breunig, M. M., Kriegel, H., and Sander, J. 1999. OPTICS: Ordering points to identify the clustering structure. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 49--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ashbrook, D. and Starner, T. 2003. Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiq. Comput. 7, 5, 275--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Badrul M., Sarwar, G. K., Joseph, A., and Konstan, J. 2001. Riedl: Item-based collaborative filtering recommendation algorithms. In Proceedings of the 3rd International Conference on World Wide Web. 285--295. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Balabanovic, M. and Shoham, Y. 1997, Fab: Content-based, collaborative recommendation. Comm. ACM 40, 3, 66--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Basu, C., Hirsh, H., and Cohen W. 2001. Recommendation as classification: Using social and content-based information in recommendation. Recommender systems papers from 1998 workshop, Tech. rep. WS-98-08, AAAI Press.Google ScholarGoogle Scholar
  7. Bikely. http://www.bikely.com!Google ScholarGoogle Scholar
  8. Bogers, T. and Bosch, A. 2007. Comparing and evaluating information retrieval algorithms for news recommendation. In Proceedings of the ACM Conference on Recommender Systems. 141--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Breese, J., S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the International 14th Conference on Uncertainty in Artificial Intelligence. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Brunato, M., Battiti, R., Villani, A., and Delal, A. 2002. A location-dependent recommender system for the Web. Tech. rep. DIT-02-093, University of Trento.Google ScholarGoogle Scholar
  11. Chen, Z., Shen, H. T., Zhou, X., Zheng, Y., and Xie, X 2010. Searching trajectories by locations: An efficiency study. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M. 1999. Combining content-based and collaborative fitters in an online newspaper. In Proceedings of the ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation.Google ScholarGoogle Scholar
  13. Counts, S. and Smith, M. 2007. Where were we: Communities for sharing space-time trails. In Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Das, A. S., Datar, M., Garg A., and Rajaram, S. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web. 271--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Getoor, L. and Sahami, M. 1999. Using probabilistic relational models for collaborative filtering. In Proceedings of the Workshop on Web Usage Analysis and User Profiling.Google ScholarGoogle Scholar
  16. Goldberg, D., David, N., Brain, M. O., and Douglas, T. 1992. Using collaborative filtering to weave an information tapestry. Comm. ACM 35, 12, 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Gonotti, F., Nanni, M., Pedreschi, D., and Pinelli, F. 2007. Trajectory pattern mining. In Proceedings of the 13rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM Press, 330--339. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Gonzalez, M. C., Hidalgo, C., A., and Barabasi, A.-L. 2008. Understanding individual human mobility patterns. Nature 453, 6, 779--780.Google ScholarGoogle ScholarCross RefCross Ref
  19. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A, Sarwar, A. B., Herlocker, J. L., and Riedl, J. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the Conference on Artificial Intelligence. AAAI Press, 439--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. GPS Sharing. http://gpssharing.com.Google ScholarGoogle Scholar
  21. GPS Track Route Exchange Forum. http://www.gpsxchange.com.Google ScholarGoogle Scholar
  22. Hariharn, R. and Toyama, K. 2004. Project Lachesis: Parsing and modeling location histories. In Proceedings of the 3rd International Conference on Geographic Information Science. 106--124.Google ScholarGoogle Scholar
  23. Hofmann, T. 2003. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press. 259--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Horozov, T., Narasimhan, N., and Vasudevan, V. 2006. Using location for personalized POI recommendations in mobile environments. In Proceedings of the International Symposium on Applicaiions on Internet. SAINT Press, 124--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jarvelin, K. and Kekalainen, J. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inform. Syst. 22, 1,422--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Krumm, J. and Horvitz, E. 2004. LOCADIO: Inferring motion and location from wi-fi signal strengths. In Proceedings of the 1st International Conference on Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services. IEEE Press, 4--13.Google ScholarGoogle Scholar
  27. Krumm, J. and Horvitz, E. 2006. Predestination: Inferring destinations from partial trajectories. In Proceedings of the 8th International Conference on Ubiquitous Computing. Springer-Verlag, 243--260. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Krumm, J. and Horvitz, E. 2007. Predestination: Where do you want to go today? IEEE Comput. Mag. 40, 4, 105--107. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Lemire, D. and MacLachlan, A. 2005. Slope One: Predictors for online rating-based collaborative filtering. In Proceedings of the SIAM Data Mining Conference. SIAM Press.Google ScholarGoogle ScholarCross RefCross Ref
  30. Li, Q., Myaeng, S., H. and Kim, B. M. 2007. A probabilistic music recommender considering user opinions and audio features. Int. J. Inform. Process. Manage. 43, 2, 473--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Li, Q., Zheng, Y., Chen, Y., Liu, W., and Ma, W. 2008. Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Liao, L., Fox, D., and Kautz, H. 2004. Learning and inferring transportation routines. In Proceedings of the National Conference on Artificial Intelligence. AAAI Press, 348--353. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Liao, L., Patterson, D. J., Fox, D., and Kautz, H. 2005. Building personal maps from GPS data. Ann. N.Y. Acad. Sci. 1093, 249--265.Google ScholarGoogle ScholarCross RefCross Ref
  34. Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet. Comput. 7, 1, 76--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Lr, Q., Zheng Y., Xie, X., Chen, Y., Liu, W., and Ma, W. 2008. Mining user similarity based on location history. In Proceedings of the 16th International Conference on Advances in Geographic Information Systems, ACM Press: 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Melville, P., Mooney, R. J., and Nagarajan, R. 2002. Content-boosted collaborative filtering for hnproved reconnnendations. In Proceedings of the 18th National Conference on Artificial Intelligence. AAAI Press, 187--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Nakamura, A. and Abe, N. 1998. Collaborative filtering using weighted majority prediction algorithms, In Proceedings of the 15th International Conference on Machine Learning. ACM Press, 395--403. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Patterson, D. J., Liao, L., Fox, D., and Kautz, H. 2003. Inferring high-level behavior from low-level sensors. In Proceedings of the 8th International Conference on Ubiquitous Computing. Springer, 73--89.Google ScholarGoogle Scholar
  39. Pazzani, M. 1999. A framework for collaborative, content-based, and demographic filtering. Artif. Intel. Rev. 13, 5, 393--408. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., and Riedl, J. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the International Conference on Computer Supported Cooperative Work. ACM Press, 175--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. Application of dimensionality reduction recommender system: A case study, In Proceedings of the ACM WebKDD Workshop.Google ScholarGoogle ScholarCross RefCross Ref
  42. Shardanand, U. and Maes, P. 1995, Social information filtering: Algorithms for automating “word of mouth.” In Proceedings of the International Conference on Human Factors in Computing Systems. ACM Press, 210--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Soboroff, I. and Nicholas, C. 1999. Combining content and collaboration in text filtering. In Proceedings of the International Joint Conference on Artificial Intelligence Workshop: Machine Learning for Information Filtering. ACM Press, 86--91.Google ScholarGoogle Scholar
  44. Spertus, E., Sahami, M., and Buyukkokten, O. 2005. Evaluating similarity measures: A large-scale study in the Orkut social network. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 678--684. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. SportsDo. 2007. http://sportsdo.net/Activity,IActivityBlog.aspx.Google ScholarGoogle Scholar
  46. Takeuchi, Y. and Sugimoto, M. 2006. CityVoyager: An outdoor recommendation system based on user location history. In Proceedings of the 3rd International Conference on Ubiquitous Intelligence and Computing. Springer, 625--636. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Tiemann, M. and Pauws, S. 2007. Towards ensemble learning for hybrid music recommendation. In Proceedings of the ACM Conference on Recommender Systems. ACM Press, 177--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Timothy, S., Varshavsky, A., Lamarca, A., Chen, M. Y., and Choudhury, T. 2006. Mobility detection using everyday GSM traces. In Proceedings of the 11th International Conference on Ubiquitous Computing. Springer, 212--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Tobler, W. 1970. A computer movie simulating urban growth in the Detroit region. Econ. Geog. 46, 2, 234--240.Google ScholarGoogle ScholarCross RefCross Ref
  50. Wang, L., Zheng, Y., Xie, X., and Ma, W. Y. 2008. A flexible spatio-temporal indexing scheme for large-scale GPS track retrieval, In Proceedings of the 9th International Conference on Mobile Data Management. IEEE Press, 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Yang, W. S., Cheng, H. C., and Dia, J. B. 2008. A location-aware recommender system for mobile shopping environments. Exp. Syst. Appl. Int. J. 437--445. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Zheng, W., Cao, B., Zheng, Y., Xie, X., and Yang, Q. 2010a. Collaborative filtering meets mobile. recommendation: A user-centered approach. In Proceedings of the 24th AAAI Conference on Artificial Intelligence. AAAl Press.Google ScholarGoogle Scholar
  53. Zheng, W., Zheng, Y., Xie, X., and Yang, Q. 2010f. Collaborative location and activity recommendations with GPS history Data. In Proceedings of the 19th International Coriference on World Wild Web. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Zheng, Y. and Xie, X. 2010c. Learning Location Correlation from GPS trajectories. In Proceedings of the International Conference on Mobile Data Management. IEEE Press, 27--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Zheng, Y. and Xie, X. 2010e. Learning travel recommendations from user-generated GPS traces. ACM Trans. Intel. Syst. Technol. 2, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Zheng, Y., Chen, Y., Li, Q., Xie, X., and Ma, W. Y. 2010b. Understanding transportation modes based on GPS data for Web applications. ACM Trans. Web. 4, 1, 1--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Zheng, Y., Chen, Y., Xie, X., and Ma, W. Y. 2009a. GeoLife2.0: A location-based social networking service. In Proceedings of the International Conference on Mobile Data Management. IEEE Press, 357--358. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W. Y. 2008a Understanding mobility based on GPS data, In Proceedings of 10th International Conference on Ubiquitous Computing. ACM Press, 312--321. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Zheng, Y., Liu, L., Wang, L., and Xie, X. 2008b. Learning transportation mode from raw GPS data for geographic applications on the Web. In Proceedings of the 11th International Conference on World Wide Web. ACM Press, 247--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Zheng, Y., Wang, L., Zhang, R., Xie, X., and Ma, W. Y. 2008c. GeoLife: Managing and understanding your past life over maps. In Proceedings of the 9th International Conference on Mobile Data Management. IEEE Press, 211--212. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Zheng, Y., Xie, X., and Ma, W. Y. 2008d. Search your life over maps. In Proceedings of the International Workshop on Mobile Information Retrieval. ACM Press, 24--27.Google ScholarGoogle Scholar
  62. Zheng, Y., Xie, X., and Ma, W. Y. 2010d. GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Engin. Bull. 33, 2, 32--40.Google ScholarGoogle Scholar
  63. Zheng, Y., Zhang, L., Xie, X., and Ma, W. Y. 2009b. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wild Web. ACM Press, 791--800. Google ScholarGoogle ScholarDigital LibraryDigital Library

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