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.