A Novel Recommendation Method Based on Rough Set and Integrated Feature Mining

The explosive growth of information makes people confused in making a choice among a huge amount of products, like movies, books, etc. To help people clarify what they want easily, in this study, we present an intelligent recommendation approach named RSCF (recommendation by rough-set and collaborative filtering) that integrates collaborative information and content features to predict user preferences on the basis of rough-set theory. The contribution of this paper is that our proposed approach can completely solve the traditional problems occurring in recent studies, such as cold-star, first-rater, sparsity and scalability problems. The empirical evaluation results reveal that our proposed approach can reduce the gap between user´s interest and recommended items more effectively than other existing approaches in terms of accuracy of recommendations.