A Systematic Review on Customer Feedback Classification Employing Deep Learning Methodologies for Recommend Systems

An efficient recommender system's (RS) significant requirement remains to give useful data concerning users and objects for producing top-grade suggestions. Written customer review remains a copious source of data, which could provide perceptions into the RS. Because of technological development, an enormous quantity of social web data rises in volume, subjectivity, and heterogeneity turns competing to prepare physically. Deep Learning (DL) approaches are employed in dealing with this arduousness in live implementations. This study attempts to demonstrate the utility, scope, and application of DL approaches' association with customer feedback in the discipline of hospitableness and touristry. A systematized literature review is exhibited for correlating, assessing, examining, and contemplating the trials and trajectories to seek research gaps in portraying the prospective scope of this pairing. The foremost intention remains to read and assess DL approaches. This study possesses important applications for service providers concerning establishing managerial strategies for customers regarding choosing services, which fulfill their requisites. Additionally, there remains a great effect on scholars concerning future research directions.