Trust-Aware Recommender System Incorporating Review Contents

Personalized recommendation systems can help people find things that interest them and are widely used in developing the Internet or e-commerce.Collaborative filtering (CF) seems to be the most popular technique in recommender systems.However, CF is weak in the process of finding similar users.To resolve these problems, trust-aware recommender systems (TaRSs) have been developed in recent years.In this study, we propose a new approach that incorporates the content of reviews in a TaRS.In addition, we use a new dataset that is collected from the Yahoo!Movie website, whereas traditional research has used Epinions or Movielens.Finally, we evaluate the experiment results using precision and coverage.