Using semantic web to reduce the cold-start problems in recommendation systems

Collaborative filtering systems suffer from the cold-start problems (evaluation matrix, new user/new resource problem...). In this paper, we show that using semantic information describing users and resources can reduce the problems and lead to a better precision, coverage and quality for the recommendation engine. Semantic web is the infrastructure used for managing such semantic descriptions. We also present here the results of a set of evaluation experiments.