改进的协同过滤算法

协同过滤是众多推荐技术中最主流的推荐技术，在个性化推荐系统中起着主导作用，然而随着大数据时代的到来，信息过载问题日益严重，评分矩阵越来越稀疏，传统协同过滤算法遇到了瓶颈。为了提高稀疏矩阵下推荐系统的推荐质量，本文对传统协同过滤算法进行改进。首先对项目集进行聚类，然后利用Slope One算法对聚类后的矩阵进行填充，最后在计算相似度时引入用户对每个聚类的喜好程度作为权重。实验结果表明，改进后的算法提高了推荐系统的推荐质量，能够有效缓解评分矩阵稀疏问题。

Improved Collaborative Filtering Algorithm

Collaborative filtering is the most widely used recommendation technology in the personalized recommendation system. However, the rapid increase of the amount of users and data make the score matrix of user's preference information become more and more sparse, and the collaborative filtering algorithm encounters a bottleneck. The calculation of similarity is the most important step in collaborative filtering algorithm. In order to improve the accuracy of the similarity of sparse matrix, this paper improves the traditional collaborative filtering algorithm. We cluster the item set first, and then use the Slope One algorithm to fill matrix after clustering, finally introduce the degree of preference of each cluster for user as the weight. The experimental results show that the improved collaborative filtering algorithm can effectively alleviate the sparse problem of scoring matrix, so as to improve the quality of the recommendation system.