iRev: Um framework de avaliação de sistemas de recomendação baseados comentários textuais

Current advances in Recommendation Systems and Natural Language Processing have motivated recent studies to return their interest in Review-Aware Recommendation Systems (RARSs). In this sense, we employ a systematic mapping approach by selecting 117 papers published on the main vehicles of the area, presenting a summary of the advances, highlighting the main proposal algorithms, and detailing the most used datasets and metrics in experimental setups. All the implementations and other artifacts extracted from this study were consolidated into a framework: iREV. In addition, we conduct a comprehensive experimental comparison among state-of-the-art proposals, highlighting the main directions and new perspectives for future developments.