User-based Clustering with Top-N Recommendation on Cold-Start Problem

Recommender system has been recognized as the most effective method for information overload problem. Although many efforts have been done on the "Cold-Start" problem, it is still an open problem and has become a very emergent issue in social network analysis. In this paper, we propose a novel approach, which applies the character capture and clustering methods to address the cold-user problem (producing recommendations to new users who have no preference on any item). We use the vector cosine method to obtain the user\´s similarity matrix and clustering users into different groups. For each group, we produce the top-N recommendation by averaging ratings of every item and choosing the top N items on the list. The experimental results on MovieLens-1M data demonstrate that our approach achieve a remarkable and consistent improvements in overcoming the cold-start problem.