Abstract
The development of location-based social networking (LBSN) services is growing rapidly these days. Users of LBSN services are more interested in sharing tips and experiences of their visits to various locations. In this paper, we aim at a study of recommending locations to users on LBSNs by collaborative filtering (CF) recommenders based only on users’ check-in data. We first design and develop a distributed crawler to collect a large amount of check-in data from Gowalla. Then, we use three ways to utilize the check-in data, namely, the binary utilization, the FIF utilization, and the probability utilization. According to different utilizations, we introduce different CF recommenders, namely, user-based, item-based and probabilistic latent semantic analysis (PLSA), to do the location recommendation. Finally, we conduct a set of experiments to compare the performances of different recommenders using different check-in utilizations. The experimental results show that PLSA recommender with the probability utilization outperforms other combinations of recommenders and utilizations for recommending locations to users on LBSN.
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References
Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Su, X., Khoshgoftaar, T.M.: A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence 2009, 4:2–4:2 (2009)
Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. Computer 42(8), 30–37 (2009)
Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative Location and Activity Recommendations with GPS History Data. In: WWW, pp. 1029–1038 (2010)
Ye, M., Ying, P., Lee, W., Lee, D.: Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. In: Proceedings of the ACM International Conference on Research and Development on Information Retrieval (SIGIR 2011), pp. 325–344 (2011)
Cho, E., Myers, S., Leskovec, J.: Friendship and Mobility: User Movement In Location-Based Social Networks. In: KDD 2011, San Diego, California, USA (2011)
Berjani, B., Strufe, T.: A Recommendation System for Spots in Location-based Online Social Networks. In: Proceedings of the 4th Workshop on Social Network Systems, Salzburg, Austria, pp. 4:1–4:6 (2011)
Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 50–57 (1999)
Scellato, S., Mascolo, C., Musolesi, M., Latora, V.: Distance Matters: Geo-Social Metrics for Online Social Networks. In: WOSN (2010)
Ye, M., Yin, P., Lee, W.C.: Location Recommendation in Location-Based Social Networks. In: GIS, pp. 458–461 (2010)
Beeharee, A., Steed, A.: Exploiting Real World Knowledge in Ubiquitous Applications. Personal and Ubiquitous Computing 11(6), 429–437 (2006)
Simon, R., Fröhlich, P.: A Mobile Application Framework for the Geospatial Web. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 381–390 (2007)
Park, M.-H., Hong, J.-H., Cho, S.-B.: Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 1130–1139. Springer, Heidelberg (2007)
Hofmann, T., Puzicha, J., Jordan, M.I.: Unsupervised Learning from Dyadic Data. In: Advances in Neural Information Processing Systems, vol. 11 (1999)
Saul, L., Pereira, F.: Aggregate and Mixed-Order Markov Models for Statistical Language Processing. In: Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing (1997)
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Zhou, D., Wang, B., Rahimi, S.M., Wang, X. (2012). A Study of Recommending Locations on Location-Based Social Network by Collaborative Filtering. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_22
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DOI: https://doi.org/10.1007/978-3-642-30353-1_22
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