Comprehensive Analysis of Deep Learning-based Customer Feedback Classification Methodologies for Recommender Systems

The provision of pertinent information about people and products is still a crucial prerequisite for an effective recommender system (RS) to provide top-notch recommendations. Published reviews online continue to be a rich repository of information that might reveal impressions of the RS. Due to technological advancement, a huge amount of interpretations and variability in social web data has given birth to physical preparation competition. In real-time domains, Machine Learning (ML) and Deep Learning (DL) innovations are used to handle this difficulty. This analysis aims to illustrate the applicability, scope, and implementation of DL techniques in the field of hospitality and tourism concerning consumer feedback. A systematic evaluation of the knowledge is presented for correlating, rating, analyzing, and reflecting on the experiments and outcomes to look for research gaps in illuminating the potential use of this combination. The major goal is still to read and evaluate DL techniques. This analysis has significant implications for service providers in terms of developing management methods for clients to select services that meet their needs. Researchers continue to have a significant impact on the areas of future research.