Recommender systems based on collaborative filtering using review texts-A survey

Abstract

In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary fromone another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recentworks that integrate reviewtexts and also discusses howthese reviewtexts are exploited for addressing some main issues of standard collaborative filtering algorithms.