Measuring serendipity

Abstract

Characterizing the relationship that exists between people's application interests and mobility properties is the core question relevant for location-based services, in particular those that facilitate serendipitous discovery of people, businesses and objects. In this paper, we apply rule mining and spectral clustering to study this relationship for a population of over 280,000 users of a 3G mobile network in a large metropolitan area. Our analysis reveals that (i) People's movement patterns are correlated with the applications they access, e.g., stationary users and those who move more often and visit more locations tend to access different applications. (ii) Location affects the applications accessed by users, i.e., at certain locations, users are more likely to evince interest in a particular class of applications than others irrespective of the time of day. (iii) Finally, the number of serendipitous meetings between users of similar cyber interest is larger in regions with higher density of hotspots. Our analysis demonstrates how cellular network providers and location-based services can benefit from knowledge of the inter-play between users and their locations and interests.