关键词: 相似度, 评分用户数量, 评分信息量, 协同过滤

Abstract: Traditional collaborative filtering computes the similarity between users based on the rating value, however, the data sparseness causes the inaccurate of similarity computing. Aiming at this problem, this paper proposes a novel method based on quantity of rating information. It translates the amount of user who rates item with the same value to quantity of rating information, and combines the item-rating to calculate the similarity. The experimental results show that the proposed can relief the effect of data sparseness and improve the accuracy of predicted rating comparing to the traditional algorithm.

Key words: similarity, amount of rating user, quantity of rating information, collaborative filtering