基于矩阵聚类的协作过滤算法

通过分析现有的协作过滤技术,提出了基于矩阵聚类的协作过滤算法,把矩阵聚类算法和协作过滤相结合,自动划分原始用户———资源评分矩阵,依据划分后的子数据矩阵生成推荐结果.实验结果表明,提出的基于矩阵聚类的协作过滤算法优于传统协作过滤算法,减少了近邻搜索范围,提高了算法的推荐精度.     

Personalized Service System Based on Hybrid Filtering for Digital Library

Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.