JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Movie Discovery with Recommendation Techniques.
Tejaswini B H, Prashant Ankalkoti
In the era of digital content expansion, efficiently finding relevant movies remains a challenge. This paper presents a movie recommendation system utilizing The Movie Database (TMDB) API to enhance user experience through personalized movie suggestions. The system retrieves comprehensive movie data, including cast, crew, genres, and ratings, and employs JavaScript for real-time data processing and an intuitive user interface. Key challenges include integrating diverse data sources and ensuring recommendation accuracy. Our approach features an auto-complete functionality for streamlined title selection and dynamically displays detailed movie information, such as cast biographies and overviews. By incorporating genre-based recommendations alongside search-based suggestions, our system effectively addresses user preferences and improves engagement. Experimental evaluation demonstrates increased recommendation accuracy and user satisfaction, highlighting the potential of API-driven solutions in delivering precise and engaging content discovery experiences.
Machine learning, Natural language processing, Movie recommended, and web-scraping.