ONLINE PLATFORM FOR MOVIE RECOMMENDATION
Ansh Tulsyan
Dept. of ECE
University of Illinois Urbana Champaign
Champaign, USA
ansht2@illinois.edu
Anshul Bhardwaj
Dept. of CSE
Chandigarh University
Mohali, India
anshulrb2004@gmail.com
Pranjal Shukla
Dept. of CSE
Chandigarh University
Mohali, India
pranjal.shukla.355@gmail.com
Jatin Verma
Dept. of CSE
Chandigarh University
Mohali, India
jatin27032003@gmail.com
Tushar Singh
Dept. of CSE
Chandigarh University
Mohali, India
Contact@itstushar.com
Abstract—It might be difficult to locate movies that suit personal tastes in the era of digital media. A state-of-the-art internet tool called CineMatch tackles this problem by providing individualized movie suggestions using sophisticated algorithmic analysis. With the use of both user input and machine learning techniques, CineMatch offers a distinctive, user-focused movie-selection experience. The complex recommendation engine at the heart of CineMatch's technology combines filtering based on content, filtering that is collaborative, and the processing of natural language (NLP). The program can evaluate large datasets of movie information, ratings from users, and watching habits thanks to this tripartite methodology. While content-based filtering looks at aspects of a film such as cast, director, and genre, collaborative filtering makes movie recommendations based on the tastes of people who are similar to one another. NLP improves the system's comprehension of user feedback and plot summaries, allowing for more nuanced recommendations. The user interface of CineMatch is simple to use and guarantees simplicity of engagement. By allowing users to rate films, provide reviews, and make watchlists, the suggestion accuracy is significantly improved. Furthermore, the portal has a 'Discover' area that highlights obscure films and promotes going outside of the mainstream.
The platform may be used for more than just personal amusement; it can also provide studios and producers with information about audience trends and preferences. CineMatch is proof of how technology can revolutionize the movie-watching experience by making it more fun, individualized, and accessible. In conclusion, CineMatch transforms how people find and enjoy movies by utilizing cutting-edge algorithms to accommodate a wide range of preferences and likes in the constantly evolving digital landscape.
Keywords—Personalized Recommendations, Machine Learning, User Interface Design, Collaborative Filtering, Content-Based Filtering, Natural Language Processing (NLP).
