Social networks as data source for recommendation systems

Abstract

Reviews and review based rankings are widely used in recommendation systems to provide potential customers quality information about selected products. During the last years, many researchers have shown that these reviews are neither objective nor do they represent real quality values. Even established ranking methods designed to fix this problem have been shown to be unreliable. In this work, user generated content of fora, weblogs and similar trustworthy social networks is proposed as an alternative data source. It is shown how this data can be used to calculate a satisfaction and relevance measure for different product features to provide potential customers reliable quality information. The method is evaluated in the automotive domain using J.D. Power's established Initial Quality Study to ensure providing meaningful quality-related data. © 2010 Springer-Verlag Berlin Heidelberg.