基于用户相似度的协同过滤算法改进

Improved collaborative filtering algorithm based on user-similarity

协同过滤技术作为目前最常见的个性化推荐技术之一,被广泛认可和应用.作为基于内容的算法执行方式,协同过滤在准确性上具有相当的优势.该算法的核心问题是相似度的计算.本论文介绍了传统协同过滤算法,并对原有的相似度公式进行了优化,使得相似度计算更具有准确性.实验表明,文中提出的优化方法在推荐精度上有显著提高,降低了平均绝对误差(Mean Absolute Error,MAE).

Collaborative filtering is widely accepted and applied currently as one of the most popular personalized recommendation methods.It is an implementation method based on content that has considerable advantages in accuracy.The core issue of collaborative filtering is how to work out the calculation of similarity.In this paper,we introduce the traditional collaborative filtering algorithm and make similarity calculation more accurately by optimizing the traditional formula of similarity.Experimental results show that the optimized algorithm can improve the accuracy of the recommendation and reduce the MAE（Mean Absolute Error,MAE） efficiently.