The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and Linked- MDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques { both content-based and collaborative filtering ones.
Exploiting the Web of Data in Model-based Recommender Systems / Di Noia, Tommaso; Mirizzi, Roberto; Ostuni, Vito Claudio; Romito, Davide. - CD-ROM. - (2012), pp. 253-256. (Intervento presentato al convegno 6th ACM Conference on Recommender Systems, RecSys 2012 tenutosi a Dublin, Ireland nel September 9-13, 2012) [10.1145/2365952.2366007].
Exploiting the Web of Data in Model-based Recommender Systems
Tommaso Di Noia;Roberto Mirizzi;Vito Claudio Ostuni;
2012-01-01
Abstract
The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and Linked- MDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques { both content-based and collaborative filtering ones.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
