A Keyphrase-Based Paper Recommender System.

Current digital libraries suffer from the information overload problem which prevents an effective access to knowledge. This is particularly true for scientific digital libraries where a growing amount of scientific articles can be explored by users with different needs, backgrounds, and interests. Recommender systems can tackle this limitation by filtering resources according to specific user needs. This paper introduces a content-based recommendation approach for enhancing the access to scientific digital libraries where a keyphrase extraction module is used to produce a rich description of both content of papers and user interests.