A Movie Recommender System Based on Semi-supervised Clustering
C. Christakou, Leonidas Lefakis, S. Vrettos, Andreas Stafylopatis · 2006
Recommender systems provide a solution to the problem of successful information searching in the reservoirs of the Internet by providing individualized recommendations. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. In this work a clustering approach based on semi-supervised learning is proposed. The method is then used to construct a recommender system for movies that combines contentbased and collaborative information. The proposed system was tested on the MovieLens data set, yielding recommendations of high accuracy.