Collaborative user modeling for enhanced content filtering in recommender systems

• Published in: Decision Support Systems 2011-11, Vol.51 (4), p.772-781
• Main Authors: Kim, Heung-Nam, Ha, Inay, Lee, Kee-Sung, Jo, Geun-Sik, El-Saddik, Abdulmotaleb
• Format: Article
• Language: English
• Subjects: Collaboration; Collaborative user modeling; Comparative analysis; Content-based user model; Decision support systems; Filtering; Filtration; Information systems; Learning; Mathematical models; Performance evaluation; Personalization; Personalized; Probability theory; Recommender system; Recommender systems; Studies; Users; Vector spaces
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2011.01.012

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.