A survey on trustworthy model of recommender system

Recommender system (RS) has evolved significantly over the last few decades. This revolutionary move in RS is the adoption of machine learning algorithms from the field of artificial intelligence to produce the personalized recommendation of products or services. This literature presents an exhaustive survey on RS to emphasizes its taxonomy pertaining to diverse perspectives. This survey aims to provide a systematic review of current research in the field of a trustworthy recommendation model and identifies research opportunities to ease the problems of cold start and data sparsity. With the emergence of the internet environment, e-commerce has widely adopted this as a strategy to identify potential customers from an ever-growing volume of online information . The influence of RS has also been flourishing due to its effectiveness in information retrieval research. This article aims to expand from the exciting phase of development in the recommender systems to its utility in the current trend of pervasive online web applications.