Research on Cold-Start Problem in User Based Collaborative Filtering Algorithm

In order to solve the cold-start problem existing in traditional user based collaborative filtering algorithm, we propose a novel user clustering based algorithm, which firstly prefills user-item rating matrix, and then considers user characteristics as well as ratings when computing user similarities, and applies optimized k-means algorithm to cluster users. MovieLens is used as the test dataset. It is proved that the algorithm proposed in this paper can solve the cold-start problem and improve the accuracy of recommendation to some extent.