POI Recommendation: A Temporal Matching between POI Popularity and User Regularity
Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: We need to examine whether the POI fits a user's availability. While there are some prior studies which included the temporal effect into POI recommendations, they overlooked the compatibility between time-varying popularity of POIs and regular availability of users, which we believe has a non-negligible impact on user decision-making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio-Temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user-POI temporal matching function. Finally, results of extensive experiments with real-world POI check-in and human mobility data demonstrate that our proposed user-POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks.
Z. Yao et al., "POI Recommendation: A Temporal Matching between POI Popularity and User Regularity," Proceedings of the IEEE 16th International Conference on Data Mining (2016, Barcelona, Spain), pp. 549-558, Institute of Electrical and Electronics Engineers (IEEE), Dec 2016.
The definitive version is available at https://doi.org/10.1109/ICDM.2016.0066
IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Barcelona, Spain)
Keywords and Phrases
Complex networks; Decision making; Baseline models; Human mobility; Location-based social networks; Personalized recommendation; Point of interest; Spatio temporal; Temporal effects; Temporal matching; Data mining
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Dec 2016