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RECSYS
2015
ACM

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

7 years 11 months ago
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
Cataldo Musto, Pierpaolo Basile, Marco de Gemmis,
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where RECSYS
Authors Cataldo Musto, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Simone Rutigliano
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