Abstract
Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However, the existing methods suffer from one or more deficiencies such as data sparsity, cold-start users, ignoring users’ specific spatial and temporal behaviors, not utilizing the shared behaviors of the users. In this paper, we propose a novel location recommendation method, namely Behavior-based Location Recommendation (BLR). BLR recommends a location to a user based on the users’ repetitive behaviors and behaviors of similar users. Additionally, to better integrate the spatial information, BLR has two spatial components, a user-based spatial component to find the spatial preferences of the user, and a behavior-based spatial component to find locations of interest for different behaviors. Experimental studies on three real-world datasets show that BLR produces better location recommendations and can effectively address data sparsity and cold-start problems.














Similar content being viewed by others
References
Rahimi, S.M., Wang, X., and Far, B. (2017) Behavior-based Location Recommendation on Location-Based Social Networks, The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2017), Jeju Island, Korea, May 23-26, 2017
Geng B, Jiao L, Gong M, Li L, Wu Y (2019) A two-step Personalized Location Recommendation based on Multi-objective Immune Algorithm. Inf Sci 475:161–181
Lian D, Zheng K, Ge Y, Cao L, Chen E, Xie X (2018) GeoMF++: Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization. ACM Trans Inf Syst 36(3):33:1–33:29
Bagci H, Karagoz P (2016) Context-aware location recommendation by using a random walk-based approach. Knowl Inf Syst 47(2):241–260
Yuan F, Guo G, Jose J, Chen L, Yu H, Chen L (2016) Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation. In: Proceeding of: 28th International Conference on Tools with Artificial Intelligence (ICTAI 2016), San Jose
Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World Wide Web (pp. 791-800). ACM
Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th international conference on World Wide Web (pp. 1029-1038). ACM
Berjani B, Strufe T (2011) A recommendation system for spots in location-based online social networks. In: Proceedings of the 4th Workshop on Social Network Systems. ACM
Zhou D, Wang B, Rahimi SM, Wang X (2012) A study of recommending locations on location-based social network by collaborative filtering. In: Advances in Artificial Intelligence (pp. 255-266). Springer Berlin Heidelberg
Ye M, Yin P, Lee WC, Lee DL (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 (pp. 325-334). ACM
Cho E, Myers S, Leskovec J (2011a) Friendship and Mobility: User Movement In Location Based Social Networks. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1082-1090. San Diego
Cho E, Myers S, Leskovec J (2011b) Data from: Friendship and Mobility: User Movement in Location Based Social Networks [dataset] available form: https://snap.stanford.edu/data/loc-brightkite.html [Accessed 19 June 2018]
Rahimi SM, Wang X (2013) Location Recommendation Based on Periodicity of Human Activities and Location Categories. In: Advances in Knowledge Discovery and Data Mining (pp. 377-389). Springer Berlin Heidelberg
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, 93100. ACM
Bao J, Zheng Y, Mokbel MF (2015) Recommendations in location-based social networks: a survey. Geoinformatica 19(3):525–565
Yu C, Liu Y, Yao D, Ding Q (2015) Mining User Check-in features for Location Classification in Location-based Social Networks. IEEE Symposium on Computers and Communication (ISCC), Larnaca, pp. 385-390
Clemente RP, Bothorel C (2013) Recommendation of shopping places based on social and geographical influences. In: RSWeb 2013: 5th ACM RecSys Workshop on Recommender Systems and the Social Web
Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) GeoMF: joint geographical modelling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '14). ACM, New York, 831-840
Yuan Q, Cong G, Sun A (2014) Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 659-668)
Park MH, Hong JH, Cho SB (2007) Location-based recommendation system using Bayesian user’s preference model in mobile devices. In: Ubiquitous Intelligence and Computing (pp. 1130-1139). Springer Berlin Heidelberg
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Jesús Bernal, (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems 26:225-238
Rahimi SM, Silva R, Far B, Wang X (2019) ORWR: Optimized random walk with restart for recommendation systems. Proceeding of the 32nd Canadian Conference on Artificial Intelligence, Kingston
Ester, M., Kriegel, H-P., Sander, J., Xu, X. (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, August 02-04, 1996, Portland, Oregon
Yang, D., Zhang, D., Qu, B., (2015a) Participatory Cultural Mapping Based on Collective Behavior Data in Location Based Social Networks. ACM Trans. on Intelligent Systems and Technology (TIST)
Yang D, Zhang D, Chen L, Qu B (2015b) NationTelescope: Monitoring and Visualizing Large-Scale Collective Behavior in LBSNs. Journal of Network and Computer Applications (JNCA) 55:170–180
Yang D. Global Check-in dataset. Available at: https://sites.google.com/site/yangdingqi/home/foursquare-dataset [Accessed 19 June 2018]
GeoCoding API by Google Maps, https://developers.google.com/maps/documentation/geocoding/start [accessed 19 June 2018]
Search API. Google API. https://developers.google.com/custom-search/json-api/v1/overview [Accessed 19 June 2018]
Kunhui L, Jingjin W, Zhongnan Z, Yating C, Zhentuan X (2015) Adaptive location recommendation algorithm based on location-based social networks. In: Proceedings of International Conference on Computer Science and Education, pp. 137–142 (2015)
Acknowledgements
The research is supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant to Xin Wang and Behrouz Far, and National Natural Science Foundation of China (No. 61772420).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rahimi, S.M., Far, B. & Wang, X. Behavior-based location recommendation on location-based social networks. Geoinformatica 24, 477–504 (2020). https://doi.org/10.1007/s10707-019-00360-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-019-00360-3