Leveraging Encyclopedic Knowledge for Transparent and Serendipitous User Profiles

摘要

The main contribution of this work is the comparison of different techniques for representing user preferences extracted by analyzing data gathered from social networks, with the aim of constructing more transparent (human-readable) and serendipitous user profiles. We compared two different user models representations: one based on keywords and one exploiting encyclopedic knowledge extracted from Wikipedia. A preliminary evaluation involving 51 Facebook and Twitter users has shown that the use of an encyclopedic-based representation better reflects user preferences, and helps to introduce new interesting topics.