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.

Meeting Name

IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Barcelona, Spain)


Computer Science


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).

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)

1550-4786; 2374-8486

Document Type

Article - Conference proceedings

Document Version


File Type





© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2016