摘要
协同过滤(CF)推荐系统应用知识发现技术为实时交易的用户提供个性化的产品或服务推荐。这些系统在电子商务领域取得了很大的成功。但是,在克服CF推荐系统的算法可伸缩性和推荐质量这两个根本性挑战方面还存在许多问题。本文分析了传统的CF算法,并介绍了一种提高推荐质量的新方法,我们称这种新方法为CF算法的推荐优化。从我们的分析可得,我们的方法相比传统的CF算法提供了更高的质量保证。
Collaborative Filtering(CF)Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for products or services during a live interaction. These systems are achieving widespread success in the E-commence or Web. However, there remain important research questions in overcoming two fundamental challenges for CF Recommender systems. They are the scalability of CF algorithms and the quality of recommendations. In this paper, we analyze the traditional CF algorithms, and introduce a novel approach to improve the quality of the recommendations for the users. We name it Optimization of Recommendations in CF Algorithms. From our analysis, it is obviously that our approach provides better quality than traditional CF algorithms.
出处
《计算机科学》
CSCD
北大核心
2004年第10期76-78,共3页
Computer Science
关键词
协同过滤
推荐系统
算法
可伸缩性
CF
实时交易
用户
项目
电子商务
产品
Collaborative filtering recommendation algorithms, Scalability, Quality of recommendations, Optimization of recommendations
