By: Adarsh S Doijode, Niral Hedau, Chaitanya Sonawane, and Abhijit T Somnathe
1-3 Student, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune
4- Assistant Professor, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune
The film industry has experienced a tremendous surge in content creation over the past few decades, leading to an overwhelming array of movies across various genres and languages. As a result, movie enthusiasts often find it challenging to discover films that align with their unique preferences and tastes. To address this issue, the application of machine learning techniques for movie recommendation has gained prominence. An introduction to the idea and process of machine learning-based movie recommendation is given in this abstract. Using data-driven methodologies and machine learning algorithms, this study aims to create an effective movie recommendation system. The system harnesses the power of user behavior data, including past movie preferences, ratings, and viewing history, to deliver personalized movie recommendations. In this field, collaborative filtering and content-based recommendation are two popular techniques. Collaborative filtering analyses user interactions and identifies similar user profiles to recommend movies that like-minded users have enjoyed. On the other side, content-based recommendation works by analyzing movie properties like genre, director, actors, and plot to recommend movies that are like the user’s previous choices.
Keywords: Movie, Algorithms, Ratings, Preferences, Genres
Citation:
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