Inspiration
We were inspired by platforms like Netflix and YouTube that make content discovery easy through smart recommendations. As a team, we wanted to understand and build a similar system using AI.
What it does
The system recommends movies based on user preferences and past ratings. It delivers fast, personalized suggestions through an interactive web interface.
How we built it
We used the MovieLens dataset and trained a deep learning–based matrix factorization model using PyTorch. The model was deployed using FastAPI and connected to a frontend for real-time recommendations.
Challenges we ran into
Handling sparse rating data and tuning model performance were challenging. Ensuring fast response times while integrating caching and APIs also required teamwork.
Accomplishments that we're proud of
We built a complete end-to-end recommendation engine with real-time inference. The system is scalable, well-documented, and production-ready.
What we learned
We gained hands-on experience with recommendation systems, deep learning, and backend APIs. Working as a team improved our collaboration and debugging skills.
What's next for AI-Powered Movie Recommendation System
We plan to improve recommendation accuracy and add more personalization features. Cloud deployment and user authentication are also planned.
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