Recommender systems usually support B2C e-Commerce activities without to provide e-buyers with information about the reputation of both products and interlocutors. To provide B2C traders with suggestions taking into account gossips, in this paper we present REBECCA, a fully decentralized trust-based B2C recommender system that also guarantees scalability and privacy. Some experiments show the advantages introduced by REBECCA in generating more effective suggestions.
REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce / Rosaci, D; Sarne', G. - 511:(2014), pp. 31-36. (Intervento presentato al convegno 7th International Symposium on Intelligent Distributed Computing - IDC 2013 tenutosi a Prague, Czech Republic nel 04-06/09/2013) [10.1007/978-3-319-01571-2_5].
REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce
ROSACI D;SARNE' G
2014-01-01
Abstract
Recommender systems usually support B2C e-Commerce activities without to provide e-buyers with information about the reputation of both products and interlocutors. To provide B2C traders with suggestions taking into account gossips, in this paper we present REBECCA, a fully decentralized trust-based B2C recommender system that also guarantees scalability and privacy. Some experiments show the advantages introduced by REBECCA in generating more effective suggestions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
