A WEB-BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

被引:60
|
作者
Lu, Jie [1 ]
Shambour, Qusai [1 ]
Xu, Yisi [1 ]
Lin, Qing [1 ]
Zhang, Guangquan [1 ]
机构
[1] Univ Technol Sydney, Decis Syst & E Serv Intelligence Lab, Ctr Quantum Computat & Intelligent Syst QCIS, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
基金
澳大利亚研究理事会;
关键词
web personalization; recommender systems; collaborative filtering; e-business; business partner; fuzzy sets; fuzzy linguistic; semantic relevance; PRODUCT TAXONOMY; SHOPBOT; DESIGN;
D O I
10.1111/j.1467-8640.2012.00427.x
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking one-to-one e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new cold start item problems.
引用
收藏
页码:37 / 69
页数:33
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