Intelligent movie recommendation system
DOI:
https://doi.org/10.56294/sctconf20251438Keywords:
Movie recommendations, Natural Language Processing, collaborative filteringAbstract
A smart piece of technology, a movie recommendation system uses intricate algorithms to deliver users personalized movie recommendations, enhancing their viewing experience. By utilizing many data sources, including user ratings, movie metadata, and viewing history, the system creates comprehensive user profiles that encompass distinct interests and behaviors. Content-based filtering, which considers storyline keywords, character, and genre, suggests movies that are similar to the ones the buyer has already enjoyed. Collaborative filtering approaches enhance suggestions even further by identifying similarities between users and suggesting movies based on those of people who are similar to each other. Hybrid approaches combine the advantages of content-based and collaborative filtering techniques to offer a wide range of accurate and varied recommendations. Continuous evaluation and user input inform algorithm refinement, maximizing the caliber of suggestions. Users may easily submit feedback, hone their preferences, and connect with other movie buffs in the community thanks to an accessible UI and interactive features. This cooperative ecology not only improves the recommendations' accuracy but also adds to the shared cultural fabric of cinematic experiences. Movie Recommendation App is a monument to creativity and innovation in a constantly changing world, transforming the way people discover and value cinematic art. It gains knowledge, adjusts, and develops with every interaction, creating a story of individualized cinematic exploration that cuts across time and genre. Come along on this thrilling adventure with us as we rethink the interaction between people and the infinite realm of filmmaking
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Copyright (c) 2025 Rajasekar R, Niranchana Radhakrishnan, Sridar K, Viji C, Mohanraj , Kalpana C, Rajkumar N (Author)
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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
