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.
Rosaci, D., Sarne', G. (2014). REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce. In Intelligent Distributed Computing VII (pp.31-36). Berlin : Springer [10.1007/978-3-319-01571-2_5].
REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce
SARNE' G
2014
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.
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