Personalized Search Based on a User-Centered Recommender Engine

Designing personalized search engines based on a recommender system that takes into consideration the user situated moment in relation to the subject matter and the context that governs user interest has been largely ignored. In this paper, we present a novel approach to integrating user interests into search within a recommender system that is guided by the semantic representation of the user and the content. In addition, our research tackles two problems of creating any recommender system: (a) the initial stage problem (how to provide recommendations to a user if the system hasn´t been used yet) and (b) user context (providing the same user with different recommendations based on the context of their recent activity). Also the design of our recommender system is modular. It integrates and accommodates user´s preferences by using User Relevance Feedback. Finally, we describe how the personalization aspects can increase the recommendation quality.