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Jia-Dong Zhang , J.-D. Zhang is with Department of Computer Science, City University of Hong Kong, Hong Kong. Y. Li is with HUAWEI Noah’s Ark Lab, Hong Kong.(
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(|L|n3) to O(|L|n) (where |L| 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.
Jia-Dong Zhang, Chi-Yin Chow, Yanhua Li, "iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework", IEEE Transactions on Services Computing, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TSC.2014.2328341
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