Personalized Movie Recommendation System

In the digital world of today, where there is an infinite amount of content to consume, including movies, books, videos, articles, and so on, finding content that appeals to one's tastes has become challenging. On the other hand, providers of digital content want to keep as many people using their service for as long as possible. This is where the recommender system comes into play, where content providers suggest content to users based on their preferences. Web applications that offer a variety of services and automatically suggest some services based on user interest increasingly rely on recommendation systems. Different business services each play a significant role in the success of the current marketing field. The personalize recommendation technique is one of the most valuable tools for providing personalized service on websites. When it comes to e-Commerce's online marketing efforts, this strategy is extremely useful. To build the proposal framework, the cooperative sifting is exceptionally helpful advances in the field of recommender frameworks. The accuracy of recommendation engines is the source of many issues in today's web. Therefore, a variety of strategies are utilized to enhance the recommendation system's diversity and accuracy. When generating recommendations, the fundamental recommender systems typically take one of the following into account: The Content-Based Filtering, which is based on the user's preferences, it describes things, and we use keywords other than the user's profile to show what the user likes and dislikes. To put it another way, CBF algorithms suggest products that people have liked in the past or products that are similar to them. It looks at what you've liked in the past and suggests the best match, Or a collaborative filtering system makes recommendations for items based on how similar users and/or items are measured. The CF system only suggests products that are popular with similar types of users. The development of a movie recommendation system with category-based recommendations, more precise results, increased efficiency, and overcoming the cold start are the goals of this system