The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively handle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed kernel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.

A Linked Data Recommender System using a Neighborhood-based Graph Kernel / Ostuni, Vito Claudio; Di Noia, Tommaso; Mirizzi, Roberto; Di Sciascio, Eugenio. - STAMPA. - (2014), pp. 89-100. (Intervento presentato al convegno 15th International Conference on E-Commerce and Web Technologies, EC-Web 2014 tenutosi a Munich, Germany nel September 1-4, 2014) [10.1007/978-3-319-10491-1_10].

A Linked Data Recommender System using a Neighborhood-based Graph Kernel

Vito Claudio Ostuni;Tommaso Di Noia;Roberto Mirizzi;Eugenio Di Sciascio
2014-01-01

Abstract

The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively handle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed kernel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.
2014
15th International Conference on E-Commerce and Web Technologies, EC-Web 2014
978-3-319-10490-4
A Linked Data Recommender System using a Neighborhood-based Graph Kernel / Ostuni, Vito Claudio; Di Noia, Tommaso; Mirizzi, Roberto; Di Sciascio, Eugenio. - STAMPA. - (2014), pp. 89-100. (Intervento presentato al convegno 15th International Conference on E-Commerce and Web Technologies, EC-Web 2014 tenutosi a Munich, Germany nel September 1-4, 2014) [10.1007/978-3-319-10491-1_10].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/20216
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