A Systematic Review on Recommender System Models, Challenges, Domains and Its Perspectives

The accelerated tremendous reach of web applications substantially raises the demand for effective recommender systems to examine and filter required content from the vast quantity of information. Recommender systems have evolved in this digital arena as a way to aid users by giving them possibilities among acceptable and relevant items by analyzing user interests. In this system, the preferences as well as prior behavior patterns of the users, have been utilized to give a recommendation. The utilization of recommendation models became a crucial component in digital marketing strategy. It also plays a vital role in areas such as streaming services (movies, music, and books), social networking systems, e-governance, e-commerce (shopping), e-library, e-learning, tourism, resource services, any group activities and much more. Recently it has been inducted into healthcare, education and a wide variety of user’s needs, to help the users in discovering and fetching related interests. However, the main challenges like cold start, sparsity, grey sheep, starvation, and shilling can degrade the performance of the recommender system. Research on recommender system has raised significantly to make the system overcome the challenges and enhance the accuracy of predictions. This article aims to provide a comprehensive review on the main models, challenges, evaluation methods, and metrics of the recommender system. Also aimed to provide a glimpse of the domains and tools concerning the recommender system. Future prospects were also to explore additional insights, and unresolved concerns in the area of RS to support future researchers.