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Recommendation Systems for Web 2.0 Marketing
Wei, Chen1; Khoury, Richard2; Fong, Simon1
Source PublicationData Mining for Service
Author of SourceKatsutoshi Yada
PublisherSpringer, Berlin, Heidelberg

Nowadays, Recommendation Systems (RS) play an important role in the e-Commerce business and they have been proposed to exploit the potential of social networks by filtering information and offering useful recommendations to customers. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommendation systems based on social networks, and social networks provide the needed collaborative social environment. CF and its variants have been studied extensively in the literature on online recommender, marketing and advertising. However, most of the works were based on Web 1.0 and in the distributed environment of Web 2.0 such as social networks, the required information by CF may either be incomplete or scattered over different sources. The system we proposed here is the Multi-Collaborative Filtering Trust Network Recommendation System, which combined multiple online sources, measured trust, temporal relation and similarity factors.

KeywordSocial Network Recommendation System Temporal Relation Customer Relationship Management Online Social Network
URLView the original
WOS SubjectBusinesscomputer Science, Interdisciplinary Applications
WOS Research AreaComputer Science ; Business & Economics
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Document TypeBook chapter
CollectionFaculty of Science and Technology
Affiliation1.Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau SAR, China
2.Department of Software Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 6R3, Canada
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wei, Chen,Khoury, Richard,Fong, Simon. Recommendation Systems for Web 2.0 Marketing:Springer, Berlin, Heidelberg,2014:171-196.
APA Wei, Chen,Khoury, Richard,&Fong, Simon.(2014).Recommendation Systems for Web 2.0 Marketing.Data Mining for Service,171-196.
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