Due to the burgeoning quantity of information in many web-based applications, recommender systems play an indispensable part in creating online suggestions for the user. In the contemporary research area of recommender systems, explainable recommendation system owns high considerations to develop high-quality recommendations with instinctive explanations to assist users to make felicitous decisions.
   Explainable recommendation systems are a class of personalized recommender systems with clarification to describe the motive of the generated user suggestions. The main significance of an explainable recommendation system is to enhance the transparency, persuasiveness, effectiveness, trustworthiness, and user satisfaction of the recommender systems.
   Deep learning models are more suitable for explainable recommendation systems. Deep learning models have automatically generated the explanation for the recommendation by analyzing the appropriate features of user reviews and items suggested. Incorporating deep learning in an explainable recommendation system produces explainable suggestions effectively according to the variegation in generating an intelligible recommendation.