摘要
协同过滤是推荐系统中广泛使用的推荐技术,研究人员对如何完善协同过滤推荐技术开展大量工作,但是相应的研究总结较少.文中对协同过滤的相关研究进行全面回顾,首先阐述协同过滤的内涵及其存在的主要问题,包括稀疏性、多内容及可扩展性,然后详细介绍国内外学者针对以上问题的解决方案,最后指出协同过滤下一步的研究重点.文中介绍一个相对完整的协同过滤知识框架,对理清协同过滤的研究脉络,为后续研究提供参考,推进个性化信息服务的发展具有一定意义.
Collaborative filtering is a widely used technique in recommender systems. Extensive studies are carried out on collaborative filtering. However, systematic summary of this field is scarce. In this paper, research of collaborative filtering is reviewed. The meaning and key issues of collaborative filtering, including sparsity, multiple-content and scalability, are described firstly, and then the solutions to the above key issues are introduced in detail. Finally, the future work of collaborative filtering is pointed out. The knowledge framework of collaborative filtering is introduced. It makes the research clues of collaborative filtering clear, provides a reference to other scholars, and improves the performance of personalized information services.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第8期720-734,共15页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.713311002
71271072)
高等学校博士学科点专项科研基金项目(No.20110111110006)
上海高校选拔培养优秀青年教师科研专项基金项目(No.sdl10021)资助
关键词
个性化服务
推荐系统
协同过滤
信息超载
Personalized Service Recommender System Collaborative Filtering Information Overload
