Abstract

In recent years there has been an explosion in the availability of data sets about human colocation with others and with specific infrastructure points, from which user location can be inferred. These traces are usually collected through mobile devices equipped with short-range radio interfaces, such as Bluetooth. Their potential is enormous as user movement data can be mapped onto the geographical space and the social interactions of individuals can be extrapolated from the colocation data. Quite interestingly, some of these data sets also contain a description of user profiles, such as the interest of the person, her age and gender and so on. In this paper we show that mobility and colocation information (i.e., social interaction) can be used to infer user interests by applying standard machine learning techniques. We evaluate a supervised and a semi-supervised technique in using two different data sets collecting information of interactions of people at conferences. We assume different degrees of available information for the inference problem and show that we are able to predict people’s interests with a very high accuracy also when only a small amount of information about user interests is available. While correlation of user interests and movement and proximity has already been investigated in social network research, this is the first work which uses machine learning to show this empirically and quantitatively. 1

Citations