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 effectiveness 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 effectiveness of our approach and suggested to further investigate this research line.
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|Titolo:||Automatic selection of linked open data features in graph-based recommender systems|
|Data di pubblicazione:||2015|
|Citazione:||Automatic selection of linked open data features in graph-based recommender systems / Musto, Cataldo; Basile, Pierpaolo; De Gemmis, Marco; Lops, Pasquale; Semeraro, Giovanni; Rutigliano, Simone. - 1448(2015), pp. 10-13. ((Intervento presentato al convegno 2nd Workshop on New Trends on Content-Based Recommender Systems, CBRecSys 2015 - co-located with 9th ACM Conference on Recommender Systems, RecSys 2015 tenutosi a aut nel 2015.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|