Content-boosted collaborative filtering for improved recommendations

摘要: Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both have their own advantages, individually they fail provide good recommendations in many situations. Incorporating components from methods, hybrid system can overcome these shortcomings. In this paper, we present an elegant and effective framework combining content collaboration. Our approach uses content-based predictor tc enhance existing user data, then provides personalized suggestions through collaborative filtering. We experimental results that show how approach, Content-Boosted Filtering, performs better than pure predictor, filter, naive approach.