Social Networks as Data Source for Recommendation Systems

摘要

Reviews and review based rankings are widely used in re-commendation systems to provide potential customers quality informa-tion about selected products. During the last years, many researchers have shown that these reviews are neither objective nor do they repre-sent real quality values. Even established ranking methods designed to fix this problem have been shown to be unreliable. In this work, user gen-erated 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 infor-mation. The method is evaluated in the automotive domain using J.D. Power's established Initial Quality Study to ensure providing meaningful quality-related data.