Title :
A Multi-clustering Hybrid Recommender System
Author :
Puntheeranurak, Sutheera ; Tsuji, Hidekazu
Author_Institution :
Tokai Univ., Tokyo
Abstract :
Recommender systems have become an important research area because they have been a kind of Web intelligence techniques to search through the enormous volume of information available on the Internet. Collaborative filtering and content-based methods are two most commonly used approaches in most recommender systems. Although each of them has both advantages and disadvantages in providing high quality recommendations, a hybrid recommendation mechanism incorporating components from both of the methods would yield satisfactory results in many situations. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data and item data, and then provides personalized suggestions through user-based collaborative filtering and item-based collaborative filtering. The proposed system clusters on content-based approach and collaborative approach then it contribute to the improvement of prediction quality of a hybrid recommender system.
Keywords :
Internet; content-based retrieval; information filtering; information filters; pattern clustering; Internet; Web intelligence; content-based predictor; item-based collaborative filtering; multiclustering hybrid recommender system; personalized suggestions; user-based collaborative filtering; Clustering methods; Collaboration; Information filtering; Information filters; Information technology; Internet; Neural networks; Recommender systems;
Conference_Titel :
Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on
Conference_Location :
Aizu-Wakamatsu, Fukushima
Print_ISBN :
978-0-7695-2983-7
DOI :
10.1109/CIT.2007.54