Abstract
In today's world, we find a wide variety of search options and we may have difficulty selecting what we really need. The recommendation System plays an important part in dealing with these problems. A recommender system is a framework that is a filtering system that filters the data with various algorithms and recommends the user with the most relevant data. Recommendation systems are productive customization mechanisms, often up-to-date and recommendations based on current consumer preferences. These systems have shown to be extremely helpful in different areas of e-commerce, education, movies, music, books, films, scientific papers, and various products. This paper reviews many approaches of recommendation techniques with their upsides and downsides and diverse performance measures. We have reviewed various articles, analyzed their technique and approach, major features of the algorithm utilized, and potential areas for improvement in that research work.
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