Abstract
Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the users to contextual variables based on their own history and the history of similar behaving users. In this paper, we propose a contextual location recommendation method named Contextual Location Recommendation (CLR) that learns the intention and spatial responses of users to various contextual triggers using the historical check-in and contextual information. CLR starts with a co-variance analysis to reduce dimensionality of the check-in data and then uses an optimized version of the random walk with restart to extract hidden user responses to contextual triggers. A tensor factorization is used to build a latent-factor model to predict the user’s intention response with the given set of contextual triggers. Based on the intention response of the user, a contextual spatial component identifies a set of matching locations accessible to the user by estimating the probability distribution of the location of the user and the popularity probability of locations under the contextual settings. Experimental results on three real-world datasets show that CLR improves the recommendation precision by 35% compared to the best-performing baseline recommendation method.
Similar content being viewed by others
References
Rahimi SM, Far B, Wang X (2019) Behavior-based location recommendation on location-based social networks. GeoInformatica. https://doi.org/10.1007/s10707-019-00360-3
Rahimi SM, Wang X, Far B (2017) Behavior-based location recommendation on location-based social networks. In: Proceedings of the PAKDD 2017, pp 273–285, DOI 10.1007/s10707-019-00360-3, (to appear in print)
Rahimi SM, Wang X (2013) Location recommendation based on periodicity of human activities and location categories. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G (eds) Advances in knowledge discovery and data mining. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 377–389
Trattner C, Oberegger A, Eberhard L, Parra D, Balby Marinho L (2016) Understanding the impact of weather for poi recommendations. In: Proceedings of the ACM RecSys workshop on recommenders in tourism (RecTour)
Xu Z, Chen L, Chen G (2015) Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing 155:99–107. https://doi.org/10.1016/j.neucom.2014.12.043. http://www.sciencedirect.com/science/article/pii/S092523121401707X
Zheng Y, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of international conference on world wide web 2009 (2009). https://www.microsoft.com/en-us/research/publication/mining-interesting-locations-and-travel-sequences-from-gps-trajectories/. WWW
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, SNS ’11. https://doi.org/10.1145/1989656.1989660. ACM, New York, pp 4:1–4:6
Zhou D, Wang B, Rahimi SM, Wang X (2012) A study of recommending locations on location-based social network by collaborative filtering. In: Proceedings of the canadian AI 2012, pp 255–266
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, SIGIR ’11. https://doi.org/10.1145/2009916.2009962. ACM, New York, pp 325–334
Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: Joint geographical modeling 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. https://doi.org/10.1145/2623330.2623638. ACM, New York, pp 831–840
Yuan F, Jose JM, Guo G, Chen L, Yu H, Alkhawaldeh RS (2016) Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation. In: 2016 IEEE 28Th international conference on tools with artificial intelligence (ICTAI). https://doi.org/10.1109/ICTAI.2016.0018, pp 46–53
Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: User movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’11. https://doi.org/10.1145/2020408.2020579. ACM, New York, pp 1082–1090
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, RecSys ’13. https://doi.org/10.1145/2507157.2507182. ACM, New York, pp 93–100
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. https://doi.org/10.1145/3182166
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. https://doi.org/10.1016/j.ins.2018.09.068. http://www.sciencedirect.com/science/article/pii/S0020025518307862
Yao L, Sheng QZ, Wang X, Zhang WE, Qin Y (2018) Collaborative location recommendation by integrating multi-dimensional contextual information. ACM Trans Internet Technol 18(3). https://doi.org/10.1145/3134438
Zhou N, Zhao W, Zhang X, Wen J, Wang S (2016) A general multi-context embedding model for mining human trajectory data. IEEE Trans Knowl Data Eng 28(08):1945–1958. https://doi.org/10.1109/TKDE.2016.2550436
Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th international conference on world wide web companion, WWW ’17 Companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https://doi.org/10.1145/3041021.3054138, pp 153–162
Yin H, Wang W, Wang H, Chen L, Zhou X (2017) Spatial-aware hierarchical collaborative deep learning for poi recommendation. IEEE Trans Knowl Data Eng 29(11):2537–2551
Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. CIKM ’16. Association for Computing Machinery, New York, pp 15–24. https://doi.org/10.1145/2983323.2983711
Rahimi SM, de Oliveira e Silva RA, Far B, Wang X (2019) Optimized random walk with restart for recommendation systems. In: Meurs MJ, Rudzicz F (eds) Advances in artificial intelligence. Springer International Publishing, Cham, pp 320–332
Global check-in dataset. https://sites.google.com/site/yangdingqi/home/foursquare-dataset. Accessed: 2018-06-19
Brightkite. https://snap.stanford.edu/data/loc-brightkite.html. Accessed: 2018-06-19
Dark sky api. https://darksky.net/dev
Lin K, Wang J, Zhang Z, Chen Y, Xu Z (2015) Adaptive location recommendation algorithm based on location-based social networks. In: 2015 10Th international conference on computer science education (ICCSE). https://doi.org/10.1109/ICCSE.2015.7250231, pp 137–142
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. Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers. Geoinformatica 26, 1–28 (2022). https://doi.org/10.1007/s10707-021-00437-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-021-00437-y