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.

作为推荐系统最成功的技术，协作过滤最近引起了极大的关注，特别是在电子商务领域。CF用于通过评估其他用户的偏好来找到最喜欢的商品，从而帮助用户找到自己喜欢的商品。稀疏的数据集捍卫了CF的效率。因此，本文提出了两种新方法，它们使用通过用户评分提供的信息来克服稀疏性问题，而无需更改尺寸。这些方法是通过基于Map-Reduce群集的CF实现的。提议的方法已通过Movielens 100K，Movielens 1M，Movielens 20M和Jester数据集进行了测试，以便与传统技术进行比较。