A recommender system framework combining neural networks & collaborative filtering

Authors:
Charalampos Vassiliou;Dimitris Stamoulis;Drakoulis Martakos;Sotiris Athanassopoulos
Affiliations:
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece;Department of Physics, Nuclear & Particle Physics Division, National and Kapodistrian University of Athens, Athens, Greece
Venue:
IMCAS'06 Proceedings of the 5th WSEAS international conference on Instrumentation, measurement, circuits and systems
Year:
2006

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Abstract

Most recommender systems use collaborative filtering or content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. An alternative method to content-based filtering could be the use of neural networks which also incorporate the essence of progressive learning as this filtering method is increasingly used by a system. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining neural networks and collaborative filtering. Our approach uses a neural network to recognize implicit patterns between user profiles and items of interest which are then further enhanced by collaborative filtering to personalized suggestions. Our preliminary study indicates that this hybrid approach is particularly promising when compared to pure content-based or collaborative filtering methods.