Aggregation Strategies for Linked Open Data-enabled Recommender Systems
Pierpaolo Basile, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Fedelucio Narducci, Giovanni Maria Semeraro · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2014
This paper provides an overview of the work done in the Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in the tasks of rating prediciton and diversity.