iGeoRec : A Personalized and Efficient Geographical Location Recommendation Framework

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

76 Scopus Citations
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Original languageEnglish
Pages (from-to)701-714
Journal / PublicationIEEE Transactions on Services Computing
Issue number5
Online published2 Jun 2014
Publication statusPublished - Sep 2015


Geographical influence has been intensively exploited for location recommendations in location-based social networks (LBSNs) due to the fact that geographical proximity significantly affects users' check-in behaviors. However, current studies only model the geographical influence on all users' check-in behaviors as a universal way. We argue that the geographical influence on users' check-in behaviors should be personalized. In this paper, we propose a personalized and efficient geographical location recommendation framework called iGeoRec to take full advantage of the geographical influence on location recommendations. In iGeoRec, there are mainly two challenges: (1) personalizing the geographical influence to accurately predict the probability of a user visiting a new location, and (2) efficiently computing the probability of each user to all new locations. To address these two challenges, (1) we propose a probabilistic approach to personalize the geographical influence as a personal distribution for each user and predict the probability of a user visiting any new location using her personal distribution. Furthermore, (2) we develop an efficient approximation method to compute the probability of any user to all new locations; the proposed method reduces the computational complexity of the exact computation method from O ( ILIn3) to O ( ILIn) (where ILI is the total number of locations in an LBSN and n is the number of check-in locations of a user). Finally, we conduct extensive experiments to evaluate the recommendation accuracy and efficiency of iGeoRec using two large-scale real data sets collected from the two of the most popular LBSNs: Foursquare and Gowalla. Experimental results show that iGeoRec provides significantly superior performance compared to other state-of-the-art geographical recommendation techniques.

Research Area(s)

  • Location-based social networks, location recommendations, probabilistic approach, personalized geographical influence, efficient approximation