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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|n 3 ) 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.

In location-based social networks (LBSNs), time significantly affects users' check-in behaviors, for example, people usually visit different places at different times of weekdays and weekends, e.g., restaurants at noon on weekdays and bars at midnight on weekends. Current studies use the temporal influence to recommend locations through dividing users' check-in locations into time slots based on their check-in time and learning their preferences to locations in each time slot separately. Unfortunately, these studies generally suffer from two major limitations: (1) the loss of time information because of dividing a day into time slots and (2) the lack of temporal influence correlations due to modeling users' preferences to locations for each time slot separately. In this paper, we propose a probabilistic framework called TICRec that utilizes Temporal Influence Correlations (TIC) of both weekdays and weekends for time-aware location recommendations. TICRec not only recommends locations to users, but it also suggests when a user should visit a recommended location. In TICRec, we estimate a time probability density of a user visiting a new location without splitting the continuous time into discrete time slots to avoid the time information loss. To leverage the TIC, TICRec considers both user-based TIC (i.e., different users' check-in behaviors to the same location at different times) and location-based TIC (i.e., the same user's check-in behaviors to different locations at different times). Finally, we conduct a comprehensive performance evaluation for TICRec using two real data sets collected from Foursquare and Gowalla. Experimental results show that TICRec achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques with temporal influence.

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users' check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L 2 TG), and utilizes the L 2 TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques. ACM Reference Format:Jia-Dong Zhang and Chi-Yin Chow. 2015. Spatiotemporal sequential influence modeling for location recommendations: A gravity-based approach.

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