iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework

• Published in: IEEE transactions on services computing 2015-09, Vol.8 (5), p.701-714
• Main Authors: Zhang, Jia-Dong, ChowMember, Chi-Yin, Li, Yanhua
• Format: Article
• Language: English
• Subjects: Approximation methods; Computational complexity; Equations; Estimation; Kernel; Mathematical model; Probabilistic logic
• ISSN: 1939-1374; 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2014.2328341

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.