[1]柯 佳,程显毅,李晓薇.基于用户反馈的智能合作过滤模型的研究[J].智能系统学报,2007,2(01):59-63.
 KE Jia,CHENG Xian-yi,LI Xiao-wei.Research of Agent collaborative filtering model based on user′s feedback[J].CAAI Transactions on Intelligent Systems,2007,2(01):59-63.
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基于用户反馈的智能合作过滤模型的研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第2卷
期数:
2007年01期
页码:
59-63
栏目:
出版日期:
2007-02-25

文章信息/Info

Title:
Research of Agent collaborative filtering model based on user′s feedback
文章编号:
1673-4785(2007)01-0059-05
作者:
柯 佳12程显毅2李晓薇2
1.江苏大学工商管理学院,江苏镇江212013;
2.江苏大学 计算机科学与通信工程学院,江苏镇江212013
Author(s):
KE Jia12 CHENG Xian-yi2 LI Xiao-wei2
1.School of Business Administration, Jiangsu University, Zhenjiang 212013, China;
 2. Computer Science & Communication Engineering Institute,Jiangsu Universi ty,Zhenjiang 212013, China
关键词:
合作过滤Agent用户兴趣Q学习
Keywords:
collaborative filtering Agent users’ interesting f eedback Q learnin g algorithm.
分类号:
TP311
文献标志码:
A
摘要:
为了提供给用户更准确的信息,提出基于用户反馈的智能合作过滤模型和一种基于用户兴趣的动态Q学习算法,并建立用户兴趣模型.通过隐式反馈和显式反馈这2种反馈方式更新用户模型并实现合作过滤.实验结果表明,在输入相同查询提问情况下ACFM在预测用户兴趣的效果和推荐搜索信息的查全率和查准率方面比传统的搜索引擎有明显改善.
Abstract:
In order to serve users with more accurate information, the Agent coll aborative filtering model—ACFM based on users’ feedback and the dynamic Q lear ning algorithm are put forward, and users’ interesting model is built. ACFM use s the method of users’ interesting feedback consisted of implicit feedback and interactive feedback to realize collaborative filtering. Experimental results sh ow that compared with traditional search engine, ACFM is more effective in predi cting users’ interests, and has more recalls and precision degree in recommendi ng information when inputting the same inquire words. 

参考文献/References:

[1]肖燕华,邵世煌. 一种基于本体论的Internet信息个性化检索系统的Agent实现模型[J].微计算机信息,2003,19(6):77-78.
 XIAO Yanhua, SHAO Shihuang. An agentrealized model of personalize internet inf ormation retrieval systerm based on ontology[J]. Control and Automation, 2003, 19(6):77-78.
[2]LEE C H, KIM Y H, RHEE P K. Web personalization expert with combinin g collaborative filtering and association rule mining technique[J].Expert Syst ems with Application,2001(21):1311-137.
[3]MELVILLE P. Contentboosted collaborative filtering for improved re commendations[A]. In Proceedings of AAAI2002[C]. Edmonton, Canada, 2002.
[4]程显毅. Agent计算[M]. 哈尔滨:黑龙江科学技术出版社,2003.【5】BOWLING M, VELOSO M. Multiagent learning using a variable lear ning rate[J]. Artificial Intelligence,2002(136):215-250.
[6】KAELBLING P L, LITTMAN L M, MOORE W A. Reinforcement learning: a survey[J]. Journal of Artificial Intelligence, 1996(4): 237-285.
[7】ZHU S H, DANA H. BALLARD. Overcoming nonstationarity in uncommunic ative learning[R]. Technical Report 762, Computer Science Dept, U. Rochester, 2001.
[8]程显毅,于冬梅.基于BDIAgent的Web搜索引擎的研究[J].江苏大学学报(自然科学版),2004,25(6):545-548.
CHENG Xianyi, YU Dongmei. A search engine of Webbased BDI Agent[J]. Journal o f Jiangsu University(Natural Science Edition),2004,25(6):545-548.
[9]曹树金,杨 涛.自动分类在搜索引擎性能优化中的应用[J].情报科学,2004, 2 2(2):214-219.
 CAO Shujin, YANG Tao. Application of automatic classification in the search engi nes optimization[J]. Information Science, 2004,22(2):213-219.
[10】KOSTOV V, NAITO E, OZAWA J. Cellular phone ring tone recommendation system based on collaborative filtering method[A]. Proceeding 2003 IEEE Inter national Symposium on computational Intelligence in Robotics and Automation[C] . Kobe, Japan,2003.

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备注/Memo

备注/Memo:
收稿日期:2006-04-07.
基金项目:国家自然科学基金资助项目(60473039)
作者简介:
柯 佳,女,1981年生,助教,硕士研究生,主要研究方向为人工智能、多Agent系统. E-mail: greenttea@163.com.
程显毅,男,1956年生,教授,博士,主要研究方向为模式识别、多Agent系统.
李晓薇,女,1982年生,助教,硕士研究生,主要研究方向为人工智能、多Agent系统.
更新日期/Last Update: 2009-05-05