POI Recommendation: A Temporal Matching between POI Popularity and User Regularity
Abstract
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.
Recommended Citation
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
Meeting Name
IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Barcelona, Spain)
Department(s)
Computer Science
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)
978-1-5090-5473-2
International Standard Serial Number (ISSN)
1550-4786; 2374-8486
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Dec 2016
Comments
This research was partially supported by National Science Foundation (NSF) via the grant number IIS-1648664. Also, it was supported in part by Natural Science Foundation of China (71329201) and the Rutgers 2015 Chancellor's Seed Grant Program. Finally, the contact author is Professor Hui Xiong (hxiong@rutgers.edu).