Authors: Nissanth Neelakandan Abirami, Sam Devavaram Jebaraj Date: November 15, 2023
Project Title: CineLit Recommender: Elevating Entertainment Discovery
Summary: CineLit is a recommendation system designed to revolutionize content discovery by seamlessly blending the worlds of cinema and literature. It delivers personalized movie suggestions using cutting-edge techniques like reinforcement learning, content-based filtering, and collaborative filtering. Additionally, CineLit extends its recommendations to include books based on users' preferred movies, creating a holistic entertainment discovery experience.
In today's vast world of entertainment content, users often face the challenge of information overload when trying to discover movies and books that match their preferences. CineLit addresses this issue by providing tailored recommendations, simplifying content discovery, and enhancing the overall user experience.
- Enhanced User Engagement: Personalized recommendations increase user engagement and retention.
- Competitive Advantage: Superior content suggestions distinguish platforms in a competitive industry.
- Increased Subscriber Base: Accurate recommendations attract new users and retain existing ones.
- Dynamic Adaptability: Reinforcement learning ensures recommendations evolve with user preferences.
This project is built upon key concepts and methodologies that shape the recommendation system:
- Content-Based Filtering: Recommending items based on their characteristics and features.
- Collaborative Filtering: Recommending items based on user preferences and behaviors.
- Reinforcement Learning: Adapting recommendations based on user interactions.
- Feed-Forward Network: A fundamental neural network architecture.
- Matrix Factorization: Decomposing user-item interaction matrices to capture latent factors.
- EASE (Explicit Alternating Least Squares): An algorithm for factorizing user-item interaction matrices.
We draw inspiration from previous research in recommendation systems, including hybrid models that combine content-based and collaborative filtering techniques for improved accuracy and performance.
We use the following datasets for movie and book recommendations:
- Movie Dataset: Comprehensive movie metadata.
- Books Dataset: Ratings for ten thousand widely-read books.
Our investigation plan includes:
- Content-based filtering for movie features.
- Collaborative filtering techniques (user-based, item-based, matrix factorization).
- Reinforcement learning for dynamic recommendations.
- Sequential recommendation systems for capturing user preferences over time.
- Scalability and resource optimization.
We will utilize GPU acceleration from platforms like Kaggle and Google Colab. Personal workstations with GPUs and CPUs will be used for development and testing. The complexity of the project may require significant computational resources.
CineLit aims to enhance user pleasure and content discovery. Expected outcomes include:
- Improved accuracy and relevance in movie and book suggestions.
- Higher user engagement and retention.
- Synergy between movie and book recommendations.
- Dynamic adaptation to user preferences over time.
- Clone this repository to your local machine.
- Follow instructions in the project's notebooks to explore and run the recommendation system.
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