Project title

Popcorn Meter

Authors

• Ethar Mohammed Hassan Ali
• Kosar Mazahery

Abstract

Popcorn Meter is a software engineering project that delivers a movie recommendation web application designed to improve the viewing experience through personalized suggestions based on each user’s preferences and interaction history. The platform allows users to explore movies, receive tailored recommendations, and maintain watchlists for future viewing. Registered users can manage profile information, set preferences, and track watched movies, enabling the system to adapt recommendations over time. The application integrates external movie databases to provide detailed information such as titles, posters, cast, genres, and ratings. Recommendation logic is currently heuristic and data-driven (rather than model-based), using user-related data stored in a relational database to rank movies with signals such as genres, cast, themes, and ratings. Recommended titles are presented with relevant metadata and quick access links where available. The watchlist feature further supports user planning and contributes to improving recommendation quality through continuous interaction data.

Disclaimer

During the preparation of this work, the authors used OpenAI Codex to refine and edit text content and to generate portions of the codebase. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the final report.