Burst the Filter Bubble: Using Semantic Web to Enable Serendipity.

Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests. However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like. To break this “personalization bubble” we introduce the notion of serendipity as a performance measure for recommendation algorithms. For this, we first identify aspects from the user perspective, which can determine level and type of serendipity desired by users. Then, we propose a user model that can facilitate such user requirements, and enables serendipitous recommendations. The use case for this work focuses on TV recommender systems, however the ultimate goal is to explore the transferability of this method to different domains. This paper covers the work done in the first eight months of research and describes the plan for the entire PhD trajectory.