Error-based collaborative filtering algorithm for top-N recommendation

Collaborative Filtering recommender system, one of the most representative systems for personalized recommendations in E-commerce, is a system assisting users in easily finding useful information. However, traditional collaborative filtering systems are typically unable to make good quality recommendations in the situation where users have presented few opinions; this is known as the cold start problem. In addition, the existing systems suffer some weaknesses with regard to quality evaluation: the sparsity of the data and scalability problem. To address these issues, we present a novel approach to provide enhanced recommendation quality supporting incremental updating of a model through the use of explicit user feedback. A model-based approach is employed to overcome the sparsity and scalability problems. The proposed approach first identifies errors of prior predictions and subsequently constructs a model, namely the user-item error matrix, for recommendations. An experimental evaluation on MovieLens datasets shows that the proposed method offers significant advantages both in terms of improving the recommendation quality and in dealing with cold start users.