Attractiveness versus Competition

Abstract

Modeling user check-in behavior provides useful insights about venues as well as the users visiting them. These insights can be used in urban planning and recommender system applications. Unlike previous works that focus on modeling distance effect on user's choice of check-in venues, this paper studies check-in behaviors affected by two venue-related factors, namely, area attractiveness and neighborhood competitiveness. The former refers to the ability of an area with multiple venues to collectively attract check-ins from users, while the latter represents the ability of a venue to compete with its neighbors in the same area for check-ins. We first embark on a data science study to ascertain the two factors using two Foursquare datasets gathered from users and venues in Singapore and Jakarta, two major cities in Asia. We then propose the VAN model incorporating user-venue distance, area attractiveness and neighborhood competitiveness factors. The results from real datasets show that VAN model outperforms the various baselines in two tasks: home location prediction and check-in prediction.