关键词: 协同过滤, 均衡接近度, 灰关联, Slope One算法, 用户相似度

Abstract:

As an item-based collaborative filtering algorithm, Slope One algorithm is not only easy to implement, but also efficient. However, the performance of the Slope One algorithm is not well when dealing with personalized recommendation tasks, which require analyzing relationships. Considering that the above problems, an improved Slope One algorithm is proposed. Firstly, this paper proposes a grey correlational analysis by the method of degree of balance approach to calculate the degree of balance between users, which measures the degree of similarity between user-pairs, and then integrates it into Slope One algorithm for rating prediction. Finally, the comparison experiments on MovieLens and Epinions datasets show that the proposed algorithm has lower Mean Absolute Error（MAE） and Root Mean Squared Error（RMSE）, improves the accuracy of prediction and recommendation quality.

Key words: collaborative filtering, degree of balance and approach, grey relational analysis, Slope One algorithm, user similarity