Building an AI-Powered Movie Recommendation System with Reinforcement Learning & RAG

Introduction

In the age of endless streaming choices, the ability to deliver personalized and engaging recommendations is what separates an average platform from an addictive one. Traditional recommendation systems rely heavily on collaborative filtering and content-based filtering, but they often struggle to adapt dynamically to user engagement.

To solve this, we're building CineSense – an AI-driven movie recommendation system that combines:

  • Retrieval-Augmented Generation (RAG) for better content discovery.
  • Reinforcement Learning (RL) to optimize recommendations based on user engagement.
  • Multi-modal data (metadata, reviews, user behavior) for deep personalization.

In this blog, we'll walk through how we built our MVP (Minimum Viable Product) in a structured, efficient manner. 🚀

Project Objectives & Success Metrics

Objectives

  • Build a RAG-powered retrieval system to find the best movie recommendations.
  • Optimize recommendations using Reinforcement Learning (DQN/PPO) based on user interactions.
  • Ensure recommendations are relevant, diverse, and explainable.

Success Metrics

  • Click-Through Rate (CTR): Percentage of recommendations clicked.
  • Watch Time: How long users engage with recommended content.
  • Engagement Score: A weighted combination of CTR & watch time.
  • Recommendation Relevance: Based on explicit user feedback (e.g., likes/dislikes).
Data Preprocessing