Item-based Collaborative Filtering Algorithm Based on Group Weighted Rating

Abstract

Item-based collaborative filtering algorithm is one of the main collaborative filtering algorithms. However, its recommendation quality is seriously influenced by the sparsity of user ratings. To solve the problem, an improved item-based collaborative filtering algorithm based on group weighted rating is proposed. The union of user rating items is used as the basis of similarity computing between items, moreover a group weighted rating method has been proposed to compute and complete the missing values in the union of user rating items for decreasing the sparsity. The experimental results show that the new algorithm can efficiently improve recommendation quality.