IGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework

Abstract

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 <i>universal </i> way. We argue that the geographical influence on users’ check-in behaviors should be <i> personalized</i> . 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 <inline-formula><tex-math>$O(|L|n^3)$</tex-math></inline-formula> to <inline-formula><tex-math> $O(|L|n)$</tex-math> </inline-formula> (where <inline-formula><tex-math>$|L|$</tex-math></inline-formula> is the total number of locations in an LBSN and <inline-formula><tex-math>$n$</tex-math></inline-formula> is the number of check-in locations of a user). Finally, we conduct extensive experiments to evaluate the recommendation <i>accuracy </i> and <i>efficiency</i> 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.