A Hybrid, Multi-dimensional Recommender for Journal Articles in a Scientific Digital Library

A recommender system for scientific scholarly articles that is both hybrid (content and collaborative filtering based) and multi-dimensional (across metadata categories such as subject hierarchies, journal clusters and keyphrases) can improve scientists´ ability to discover new knowledge from a digital library. Providing users with an interface which enables the filtering of recommendations across these multiple dimensions can simultaneously provide explanations for the recommendations and increase the user´s control over how the recommender behaves.