Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers

Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the users to contextual variables based on their own history and the history of similar behaving users. In this paper, we propose a contextual location recommendation method named Contextual Location Recommendation (CLR) that learns the intention and spatial responses of users to various contextual triggers using the historical check-in and contextual information. CLR starts with a co-variance analysis to reduce dimensionality of the check-in data and then uses an optimized version of the random walk with restart to extract hidden user responses to contextual triggers. A tensor factorization is used to build a latent-factor model to predict the user’s intention response with the given set of contextual triggers. Based on the intention response of the user, a contextual spatial component identifies a set of matching locations accessible to the user by estimating the probability distribution of the location of the user and the popularity probability of locations under the contextual settings. Experimental results on three real-world datasets show that CLR improves the recommendation precision by 35% compared to the best-performing baseline recommendation method.