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ABSTRACT
Due to social media, e-commerce, and the general digitization of businesses, there has been an explosion in data during the past ten years. The information is used to forecast market trends and patterns in customer preferences and to make well-informed decisions. Since the widespread use of internet services, recommendation systems have proliferated. The goal is to employ filtering and clustering algorithms to propose content to visitors that they may find interesting. Users are given recommendations for media products like movies by locating user profiles of people with similar likes. By initially allowing users to rate the movies of their choosing, user preference is ascertained. After some use, the recommender system will be better able to identify the user and make recommendations for movies that are more likely to receive higher ratings. In comparison to other models, the experiment findings on the TMDB 5000 Movie dataset offer a trustworthy model that is accurate and produces more individualized movie suggestions
