A Highly Automated Recommender System Based on a Possibilistic Interpretation of a Sentiment Analysis

Abstract

This paper proposes an original recommender system (RS) based upon an automatic extraction of trends from opinions and a multicriteria multi actors assessment model. Our RS tries to optimize the use of the available information on the web to reduce as much as possible the complex and tedious steps for multicriteria assessing and for identifying users' preference models. It may be applied as soon as i) overall assessments of competing entities are provided by trade magazines and ii) web users' critics in natural languages and related to some characteristics of the assessed entities are available. Recommendation is then based on the capacity of the RS to associate a web user with a trade magazine that conveys the same values as the user and thus represents a reliable personalized source of information. Possibility theory is used to take account subjectivity of critics. Finally a case study concerning movie recommendations is presented. © Springer International Publishing Switzerland 2014.