Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
DTMF: A User Adaptive Model for Hybrid Recommendation
verfasst von : Wenlong Yang, Jun Ma, Shanshan Huang
Erschienen in: Web-Age Information Management
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
Due to the uneven distributions of the ratings and social relationships for each user, two types of above recommendation methods should have varying weights when make recommendations. In this paper, we propose a user adaptive hybrid recommendation model, which dynamically combines a trust-aware based method and low-rank matrix factorization with adaptive tradeoff parameters, named as DTMF. It can utilize the advantages of these two methods and learn combinative parameters automatically. We investigate our model’s performance on two social data sets - Epinions and Flixster. Experimental results show that DTMF performs better than other state-of-the-art methods.