Recommendation Technique-based Government-to-Business Personalized e-Services

被引：0

作者：

Lu, Jie ^{[1
]}

Shambour, Qusai ^{[1
]}

Zhang, Guangquan ^{[1
]}

机构：

[1] Univ Technol Sydney, Lab Decis Syst & E Serv Intelligence, Fac Engn & Informat Technol, Broadway, NSW 2007, Australia

来源：

2009 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY | 2009年

关键词：

Recommender Systems; Personalization; E-government; E-Services; Fuzzy sets; Collaborative Filtering;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners.

引用

页码：404 / 409

页数：6

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