Multi-source Provenance-aware User Interest Profiling on the Social Semantic Web

Abstract

The creation of accurate user profiles of interest across heterogeneous websites is a fundamental step for personalisation, recommendations and analysis of social networks. The opportunities offered by the Web of Data and Semantic Web technologies introduce new interesting challenges. In particular, the main benefits for user profiling techniques are given by the extensive amount of already available and structured information and the solution to the “cold start” problem. On the other hand it is difficult to manage a massive “open corpus” such as the Web of Data and select only the relevant features and sources from an heterogeneous collection of datasets. Hence we propose semantic technologies for interlinking social websites and provenance management on the Web of Data to retrieve accurate information about data producers. The goal is to build comprehensive user profiles based on qualitative and quantitative measures about user activities across social sites.