Linked Open Data-enabled Strategies for Top-N Recommendations.
Cataldo Musto, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Maria Semeraro · 2014
The huge amount of interlinked information referring to dif-ferent domains, provided by the Linked Open Data (LOD) initiative, could be e↵ectively exploited by recommender sys-tems to deal with the cold-start and sparsity problems. In this paper we investigate the contribution of several features extracted from the Linked Open Data cloud to the accuracy of di↵erent recommendation algorithms. We focus on the top-N recommendation task in presence of binary user feedback and cold-start situations, that is, predicting ratings for users who have a few past ratings, and predicting ratings of items that have been rated by a few users. Results show the potential of Linked Open Data-enabled approaches to outperform existing state-of-the-art algorithms.