Crowdsourcing Recommendations From Social Sentiment
In this paper, the authors investigate an innovative recommendation system by incorporating relevant social opinion and sentiment information. Their recommendation system, a powerful application of social sentiment analysis, differs from many existing models, which investigate the situation where the social network itself is structured to work with the product ranking and is specially built inside an e-commerce website. In contrast, their proposed system focuses on constructing and inferring product recommendations from external Social Network Services (SNS) such as Facebook. In their system, they process product features in a finite-dimensional polynomial linear space. Additional components of their proposed system include an asymmetric similarity measurement and an asymmetric advantage measurement.