Modeling check-in behavior with geographical neighborhood influence of venues

Abstract

With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4, 6, 12, 17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby neighbors in order to gain visitation from users. In this paper, through an extensive empirical study, we show that these two geographical effects, together with social homophily, play significant roles in understanding users’ check-in behaviors. From the observation, we then propose to model users’ check-in behavior by incorporating these effects into a matrix factorization-based framework. To evaluate our proposed models, we conduct check-in prediction task and show that our models outperform the baselines. Furthermore, we discover that neighborhood competition effect has more impact to the users’ check-in behavior than spatial homophily. To the best of our knowledge, this is the first study that quantitatively examine the two effects of geographical neighborhood influence on users’ check-in behavior.