Review-Aware Recommender Systems (RARSs): Recent Advances, Experimental Comparative Analysis, Discussions, and New Directions

Recommender systems (RSs) have emerged as an effective strategy for dealing with information overload. Their importance is undeniable as they are widely adopted in various web applications, presenting the potential to solve various problems associated with the abundance of choices. In recent years, the literature has witnessed the proposal of numerous recommendation techniques, constantly seeking to improve the effectiveness of these methods. Although many approaches have focused on techniques that employ quantitative (numerical) evaluations, it is essential to recognize the significant value of the user feedback , usually expressed as (textual) comments (a.k.a., reviews) in order to understand their preferences. In this context, various algorithms have been developed to effectively take advantage of comments as a valuable source of information for Review-Aware RSs (RARSs), capable of generating recommendations in line with each user’s profile using their respective comments. This article aims to review the primary research efforts on RARSs comprehensively. We present a taxonomy of recommendation models in this domain, accompanied by a detailed summary of the main advances, the main datasets, and the used metrics. In addition, we conduct a comprehensive experimental comparison among state-of-the-art proposals, highlighting the main directions and new perspectives for future developments. Code and datasets of our open benchmark are available at https://github.com/guibitten03/iRev for fostering replicability and new advances.