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.
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Wei, C., Khoury, R., Fong, S. (2014). Recommendation Systems for Web 2.0 Marketing. In: Yada, K. (eds) Data Mining for Service. Studies in Big Data, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45252-9_11
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DOI: https://doi.org/10.1007/978-3-642-45252-9_11
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