A Collaborative Approach to User Modeling for Personalized Content Recommendations

Abstract

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of contents that are likely to fit their needs. One notable challenge in a recommender system is the cold start problem. To address this issue, we propose a collaborative approach to user modeling for generating personalized recommendations for users. Our approach first discovers useful and meaningful patterns of users, and then enriches a 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-based collaborative filtering, and vector space model. We present experimental results that show how our model performs better than existing work.