An improved collaborative filtering model based on rough set

Abstract

Collaborative filtering has been proved to be one of the most successful techniques in recommender system. However, a rapid expansion of Internet and e-commerce system has resulted in many challenges. In order to alleviate sparsity problem and recommend more accurately, a collaborative filtering model based on rough set is proposed. The model uses rough set theory to fill vacant ratings firstly, then adopts rough user clustering algorithm to classify each user to lower or upper approximation based on similarity, and searches the target user’s nearest neighborhoods and make top-N recommendations at last. Well-designed experiments show that the proposed model has smaller MAE than traditional collaborative filtering and collaborative filtering based on user clustering, which indicates that the proposed model performs better, and can improve recommendation accuracy effectively.