Automatic Selection of Linked Open Data Features in Graph-based Recommender Systems

In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically, we investigated whether the integration of LOD-based features can improve the e↵ectiveness of a graph-based recommender system and to what extent the choice of the features selection technique can influence the behavior of the algorithm by endogenously inducing a higher accuracy or a higher diversity. The experimental evaluation showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, our algorithm fed with LODbased features was able to overcome several state-of-the-art baselines: this confirmed the e↵ectiveness of our approach and suggested to further investigate this research line. Keywords Recommender Systems, PageRank, Graphs, Linked Open Data, Feature Selection, Diversity

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Cataldo Musto, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Simone Rutigliano
• Keywords: Control Systems, RECSYS 2015