[1]姜雅倩 王直杰 张珏.基于供求关系及协同过滤技术的推荐模型研究[J].计算机技术与发展,2007,(06):18-21.
　JIANG Ya-qian,WANG Zhi-jie,ZHANG Jue.Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering[J].,2007,(06):18-21.
点击复制

基于供求关系及协同过滤技术的推荐模型研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2007年06期
页码:
18-21
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering
文章编号:
1673-629X（2007）06-0018-04
作者:
姜雅倩 王直杰 张珏
东华大学信息科学与技术学院
Author(s):
JIANG Ya-qian;  WANG Zhi-jie;  ZHANG Jue
College of Information Sciences and Technology, Donghua University
关键词:
供求关系; 协同过滤; 个性化; 推荐
Keywords:
supply and demand;  collaborative filtering;  personalization;  recommendation
分类号:
TP18
文献标志码:
A
摘要:
推荐系统已经成功地应用于电子商务、数字图书馆等方面。但随着近年来公共服务平台的发展,现存的推荐系统不能有效处理公共服务平台中不同类型企业之间供求关系的推荐问题,不能针对供求关系产业链做出准确、迅速的推荐。因此,根据公共服务平台的供求关系产业链并结合协同过滤技术,提出了一种新的个性化推荐模型,它基于网络平台中的企业分类、供求关系等来建立模型,并通过建立企业类用户群来缩小协同过滤时用户群体的数量,降低计算时属性空间的维度,从而提高推荐的效率。使用该模型进行推荐可以更好地帮助企业建立沟通渠道、获得服务信息,满足
Abstract:
Recommendation systems have gained successful applications in E - commerce, digital library and other domains. However, with the development of public service platform, existing recommendation systems can not effectively deal with recommendation issue of the supply and demand relation occurred in different kinds of enterprises and can not make proper and quick recommendations referring to supply and demand relation of industry chain. Thus, combining the feature of public service platform and collaborative filtering, presents a novel personalized recommendation model. The model is created on the basis of the enterprises categories and the supply and demand relation. By establishing enterprises user group, the model reduces the quantity of the user group when collaborative filtering, and eventually improves the efficiency of recommendation. Using this model for recommendation can make great help for the enterprises to establish communication channel and obtain the service information

相似文献/References:

[1]邵延振 蒙韧 袁鼎荣 李新友.基于Web结构分区的协同过滤推荐算法研究[J].计算机技术与发展,2010,(06):67.
　SHAO Yan-zhen,MENG Ren,YUAN Ding-rong,et al.Collaborative Filtering Recommendation Algorithm Research Based on Web Blocks[J].,2010,(06):67.
[2]查文琴 梁昌勇 曹镭.基于用户聚类的协同过滤推荐方法[J].计算机技术与发展,2009,(06):69.
　ZHA Wen-qin,LIANG Chang-yong,CAO Lei.Collaborative Filtering Recommendation Method Based on Clustering of Users[J].,2009,(06):69.
[3]游文 叶水生.电子商务推荐系统中的协同过滤推荐[J].计算机技术与发展,2006,(09):70.
　YOU Wen,YE Shui-sheng.A Survey of Collaborative Filtering Algorithm Applied in E- commerce Recommender System[J].,2006,(06):70.
[4]徐红 彭黎 郭艾寅 徐云剑.基于用户多兴趣的协同过滤策略改进研究[J].计算机技术与发展,2011,(04):73.
　XU Hong,PENG Li,GUO Ai-yin,et al.User-Based Collaborative Filtering Strategies More Interested in Improvement of Research[J].,2011,(06):73.
[5]杨东风 牛永洁.基于混合规则的图书推荐模型设计与研究[J].计算机技术与发展,2011,(07):210.
　YANG Dong-feng,NIU Yong-jie.Books Recommended Model Design and Research Based on Mixing Rules[J].,2011,(06):210.
[6]吴月萍 王娜 马良.基于蚁群算法的协同过滤推荐系统的研究[J].计算机技术与发展,2011,(10):73.
　WU Yue-ping,WANG Na,MA Liang.Research of Collaboration Filtering Recommendation System Based on Ant Algorithm[J].,2011,(06):73.
[7]李克潮,蓝冬梅.一种属性和评分的协同过滤混合推荐算法[J].计算机技术与发展,2013,(07):116.
　LI Ke-chao,LAN Dong-mei.A Collaborative Filtering Hybrid Recommendation Algorithm for Attribute and Rating[J].,2013,(06):116.
[8]范虎,花伟伟.协同过滤推荐算法的研究与改进[J].计算机技术与发展,2013,(09):66.
　FAN Hu[],HUA Wei-wei[].Research and Improvement of Collaborative Filtering Recommendation Algorithm[J].,2013,(06):66.
[9]李振博,徐桂琼,査九. 基于用户谱聚类的协同过滤推荐算法[J].计算机技术与发展,2014,24(09):59.
　LI Zhen-bo,XU Gui-qiong,ZHA Jiu. A Collaborative Filtering Recommendation Algorithm Based on User Spectral Clustering[J].,2014,24(06):59.
[10]李荟,谢强,丁秋林. 一种基于情景的协同过滤推荐算法[J].计算机技术与发展,2014,24(10):42.
　LI Hui,XIEQiang,DING Qiu-lin. A Collaborative Filtering Recommendation Algorithm Based on Scenario[J].,2014,24(06):42.

备注/Memo

备注/Memo:
上海市科委资助项目（04BZ11068）姜雅倩（1982-）,女,山东莱阳人,硕士研究生,主要研究方向为Web数据挖掘、个性化服务; 王直杰,教授,主要研究方向为企业信息化、神经网络理论与应用
更新日期/Last Update: 1900-01-01