Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
RSOL: A Trust-Based Recommender System with an Opinion Leadership Measurement for Cold Start Users
verfasst von : Jiun-Yuan Wang, Hung-Yu Kao
Erschienen in: Information Retrieval Technology
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The cold start problem is a potential issue in computer-based information systems that involve a degree of automated data modeling. Specifically, the system cannot infer a rating for users or items that are new to the recommender system when no sufficient information has been gathered. Currently, more websites are providing the relationships between users, e.g., the trust relationships, to help us alleviate the cold start problem. In this paper, we proposed a trust-based recommender model (
RSOL
) that is able to recognize the user’s recommendation quality for different items. A user’s recommendation quality contains two parts: “
Rating Confidence
”- an indicator of the user’s reliability when rating an item, and “
Proximity Prestige
”- an indicator of the user’s influence on a trust network. In our experimental results, the proposed method outperforms the
Collaborative Filtering
and trust-based methods on the Epinions dataset.