360 USER PROFILE LEARNING FROM MULTIPLE SOCIAL NETWORKS FOR WELLNESS AND URBAN MOBILITY APPLICATIONS

Abstract

The thesis focused on investigating user profiling across multiple social networks in Wellness and Urban Mobility domains. Considering that user profiling can be performed at individual and group levels, this thesis proposes two multi-source learning schemes: multi-source learning scheme for individual user profiling and multi-source community detection scheme for group user profiling. We practically applied the proposed approaches in three user profiling scenarios: demographic profiling, physical wellness profiling, and venue category recommendation.

The experimental results enable us to draw the following three key findings. First, utilization of multiple data sources improves the performance of individual and group user profiling as well as their applications. Second, it is important to take inter-category relatedness into account when dealing with multiple social networks and sensor data simultaneously. Third, in the context of group user profiling, consideration of inter-network relationship is essential.