LORE: exploiting sequential influence for location recommendations
Providing location recommendations becomes an importan-t feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical in uence and social in uence have been intensively used in location rec-ommendations based on the facts that geographical proxim-ity of locations significantly affects users ’ check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any se-quential in uence of locations on users ’ check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). LORE then predicts the probability of a user visiting a loca-tion by Additive Markov Chain (AMC) with L2TG. Finally, LORE fuses sequential in uence with geographical in uence and social in uence into a unified recommendation frame-work; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets col-lected from Foursquare and Gowalla. Experimental result-s show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recom-mendation techniques.