Abstract
Location recommendation is a popular service for location-based social networks. This service suggests unvisited sites to the users based on their visiting history and site information. In this paper, we first present how to build the temporal and spatial probability distribution functions (PDF) to model the temporal and spatial checkin behavior of the users. Then we propose two recommender algorithms, Probabilistic Category Recommender (PCR) and Probabilistic Category-based Location Recommender (PCLR), based on the periodicity of user checkin behavior. PCR uses the temporal PDF to model the periodicity of users’ checkin behavior. PCLR combines the temporal category model used in PCR with a geographical influence model built on the spatial PDF. The experimental results show that the proposed methods achieve better precision and recall than two well-known location recommendation methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Ye, M., Ying, P., Lee, W., Lee, D.: Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. In: 34th ACM International Conference on Research and Development on Information Retrieval, Beijing, China, pp. 325–344 (2011)
Cho, E., Myers, S., Leskovec, J.: Friendship and Mobility: User Movement In Location-Based Social Networks. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, pp. 1082–1090 (2011)
Cheng, Z., Caverlee, J., Lee, K., Sui, D.: Exploring millions of footprints in location sharing services. In: 5th International Conference on Weblogs and Social Media, Barcelona, Spain, pp. 81–88 (2011)
Zhou, D., Wang, B., Rahimi, S.M., Wang, X.: A Study of Recommending Locations on Location-Based Social Network by Collaborative Filtering. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 255–266. Springer, Heidelberg (2012)
Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative Location and Activity Recommendations with GPS History Data. In: 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, pp. 1029–1038 (2010)
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)
Simon, R., Frőhlich, P.: A Mobile Application Framework for the Geospatial Web. In: 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 381–390 (2007)
Beeharee, A., Steed, A.: Exploiting Real World Knowledge in Ubiquitous Applications. Personal and Ubiquitous Computing Archive 11(6), 429–437 (2007)
Wang, J., Prabhala, B.: Periodicity Based Next Place Prediction. In: Workshop on Mobile Data Challenge by Nokia, Newcastle, UK (2012)
Bao, J., Zheng, Y., Mokbel, M.: Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data. In: 20th ACM SIGSPATIAL International Conference on Advances in GIS. Redondo Beach, California (2012)
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 1099–1108 (2010)
Li, Z., Wang, J., Han, J.: Mining event periodicity from incomplete observations. In: 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 444–452 (2012)
Eagle, N., Pentland, A.: Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology 63, 1057–1066 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rahimi, S.M., Wang, X. (2013). Location Recommendation Based on Periodicity of Human Activities and Location Categories. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)