A Slope One and Clustering based Collaborative Filtering Algorithm

Collaborative filtering is the most successful and widely used technology in E-commerce recommendation system. However, the traditional collaborative filtering recommendation algorithm faces severe problems of sparse user ratings and poor scalability. Slope One algorithm can reduce the sparsity of ratings, improve the recommendation accuracy, but with the growth of users and items, the running time increases rapidly. In this paper, we first introduce the feature similarity into Slope One algorithm, then combine it with ants clustering algorithm, thus reliving the influence of rating sparsity, improving the searching speed, and reducing the searching costs. Experimental results show that the new algorithm can efficiently improve recommendation quality.