MAYA

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  Working Demonstration image.

Inspiration

The idea for MAYA was born out of a love for cinema and the ever-growing struggle to find the next great movie to watch. With streaming platforms offering endless choices, it often becomes overwhelming to decide what to watch next. This led to the creation of MAYA—a system designed to take the stress out of decision-making by delivering personalized movie recommendations, perfectly tailored to individual tastes.

What it does

MAYA is a movie recommendation system that uses advanced machine learning techniques to suggest movies that users are likely to enjoy. It analyzes users' viewing history, preferences, and even trends in the movie industry to provide relevant and diverse movie suggestions. Whether you're in the mood for a hidden gem or a blockbuster hit, MAYA curates a list of movies just for you.

How we built it

MAYA was built using a combination of powerful technologies:

• Python served as the backbone, handling data processing and algorithm implementation.
• Streamlit was chosen for its simplicity and effectiveness in creating a user-friendly web interface, allowing users to interact with MAYA effortlessly.
• Pandas and NumPy were utilized for data manipulation, enabling the system to handle large datasets efficiently.
• Scikit-Learn and the Surprise Library were key in implementing collaborative and content-based filtering, as well as advanced matrix factorization techniques for improved recommendations.
• A hybrid model was developed by combining content-based and collaborative filtering methods to enhance the accuracy and relevance of the recommendations.

Challenges we ran into

Creating a recommendation system that delivers both accuracy and variety was a major challenge. Balancing between suggesting popular titles and uncovering lesser-known movies required fine-tuning the algorithms. Additionally, optimizing the system to process large amounts of data while maintaining a fast response time was a technical hurdle that needed to be addressed.

Accomplishments that we're proud of

We are particularly proud of the hybrid recommendation model in MAYA. By blending different recommendation techniques, we were able to create a system that not only offers accurate suggestions but also introduces users to new and unexpected movies they might love. The smooth and interactive interface built with Streamlit is another highlight, making the user experience both intuitive and enjoyable.

What we learned

The development of MAYA taught us valuable lessons in machine learning, data processing, and user experience design. We learned the importance of balancing different recommendation strategies to achieve the best results and gained insights into optimizing performance for large-scale applications. The project also emphasized the significance of user feedback in refining and improving the system.

What's next for MAYA

The next steps for MAYA include integrating real-time data updates to keep recommendations fresh and relevant. We also plan to incorporate user feedback mechanisms, allowing MAYA to learn and adapt to individual preferences over time. Additionally, exploring integration with social media platforms could provide even richer contextual data, further enhancing the personalization of recommendations. Expanding MAYA to include recommendations for TV shows and web series is another exciting avenue we're looking forward to exploring.

Built With

• numpy
• pandas
• python
• scikit-learn
• streamlit