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互联网推荐系统比较研究
引用本文:许海玲,吴 潇,李晓东,阎保平.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362.
作者姓名:许海玲  吴 潇  李晓东  阎保平
作者单位:1. 中国科学院,计算机网络信息中心,CNNIC实验室,北京,100190
2. 中国科学院,计算技术研究所,智能信息处理重点实验室,北京,100190
3. 中国科学院,计算机网络信息中心,CNNIC实验室,北京,100190;中国科学院,计算机网络信息中心,北京,100190
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60773056, 60802028, 60873165 (国家自然科学基金); the National Basic Research Program of China under Grant No.2007CB311100 (国家重点基础研究发展计划(973)); the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z416 (国家高技术研究发展计划(863)); the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant No.CNIC_QN_07001 (中国科学院知识创新工程青年人才领域前沿项目); the Beijing New Star Project on Science & Technology of China under Grant No.2007B071 (北京市科技新星计划)
摘    要:全面地总结推荐系统的研究现状,旨在介绍网络推荐的算法思想、帮助读者了解这个研究领域.首先阐述了推荐系统研究的工业需求、主要研究机构和成果发表的期刊会议;在讨论了推荐问题的形式化和非形式化定义之后,对主流算法进行了分类和对比;最后总结了常用数据集和评测指标,领域的重难点问题和未来可能的研究热点.

关 键 词:推荐系统  社会网络  信息过载  协同过滤  个性化
收稿时间:2008/1/22 0:00:00
修稿时间:5/5/2008 12:00:00 AM

Comparison Study of Internet Recommendation System
XU Hai-Ling,WU Xiao,LI Xiao-Dong and YAN Bao-Ping.Comparison Study of Internet Recommendation System[J].Journal of Software,2009,20(2):350-362.
Authors:XU Hai-Ling  WU Xiao  LI Xiao-Dong and YAN Bao-Ping
Institution:1+;1;3;Laboratory of CNNIC;Computer Network Information Center;The Chinese Academy of Sciences;Beijing 100190;China;Key Laboratory of Intelligent Information Processing;Institute of Computing Technology;China;Computer Network Information Center;China
Abstract:This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
Keywords:recommender system  social network  information overload  collaborative filtering  personalization
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