Two new collaborative filtering approaches to solve the sparsity problem

Collaborative filtering which is the most successful technique of the Recommender System, has recently attracted great attention, especially in the field of e-commerce. CF is used to help users find their preferred items by assessing the preferences of other users to find most similar to the active one. Sparse datasets defend the efficiency of CF. Therefore this paper proposes two new methods that use the information provided via user ratings to overcome the sparsity problem without any change of dimension. The methods are implemented via Map-Reduce clustering-based CF. The proposed approaches have been tested by Movielens 100K, Movielens 1M, Movielens 20M, and Jester datasets in order to make a comparison with the traditional techniques. The experimental results show that the proposed methods can lead to improved performance of the Recommender System.