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Recommendation Systems for Web 2.0 Marketing

  • Chen WeiEmail author
  • Richard Khoury
  • Simon Fong
Chapter
Part of the Studies in Big Data book series (SBD, volume 3)

Abstract

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.

Keywords

Social Network Recommendation System Temporal Relation Customer Relationship Management Online Social Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaChina
  2. 2.Department of Software EngineeringLakehead UniversityThunder BayCanada

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