Building My Hybrid Movie Recommendation System: A Beginner’s Perspective

As someone exploring data science and machine learning, I’ve always been drawn to projects that solve everyday problems. One of those problems? Spending way too long scrolling through movies, unable to decide what to watch.

This inspired me to build my own Hybrid Movie Recommendation System, where I could apply real machine learning concepts, while creating something useful — a system that combines multiple approaches to provide better suggestions, tailored to each user.

This project, called The Screening Room, was a collaborative effort with my teammate Prashasti, and it became a great hands-on way to move from theory to practical implementation.

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Why We Built This

Most recommendation systems follow either:

• Content-based filtering — Suggesting similar movies based on their attributes (like cast, director, genre)

• Collaborative filtering — Suggesting movies based on what users with similar taste enjoy

But both methods have limitations:

• Content-based alone gets repetitive

• Collaborative filtering struggles with new users or lesser-known movies

That’s where the hybrid approach comes in — combining both methods for more balanced, diverse recommendations.

As a beginner, this project allowed me to understand how these techniques work practically, beyond just theoretical concepts.

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The Project — Overview

We called it The Screening Room — a web-based, hybrid movie recommendation system with additional user-centric features.

Key Features:
✅ Content-based recommendations using movie metadata
✅ Collaborative filtering with KNN and user-movie ratings
✅ Mood-based suggestions for more personalized recommendations
✅ Watchlist management (Plan to Watch, Watching, Watched, Dropped)
✅ User profiles with persistent recommendations and history
✅ Search-based suggestions
✅ Clean, beginner-friendly UI

This was a team project. I focused on building the hybrid logic, content-based recommendation module, collaborative filtering system, and rating integration, while Prashasti developed the website and watchlist-based recommendation logic.

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Understanding the Hybrid Approach

1️⃣ Content-Based Filtering

• We combined movie features like title, cast, director, genre

• Used TF-IDF Vectorization to extract important terms

• Applied Cosine Similarity to find similar movies based on content

This ensures recommendations stay relevant to the user’s movie preferences.

2️⃣ Collaborative Filtering

• We created a user-movie rating matrix

• Implemented K-Nearest Neighbors (KNN) with cosine similarity

• Suggested movies based on what similar users liked

3️⃣ Hybrid Recommendation Logic

The system merges both models, combining content-based suggestions with collaborative filtering results to generate more diverse recommendations.

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Additional Features

To make the system feel more complete for users, we added:

✅ Mood-Based Suggestions: Filter movies based on moods or genres

✅ Watchlist Management: Track your movies as Plan to Watch, Watching, Watched, or Dropped

✅ User Profiles: Save watch history, personal ratings, and preferences

✅ Search-Based Suggestions: Refine recommendations based on search input

✅ Movie Details Page: A dedicated page that displays enriched movie information, including overview, runtime, cast & crew, budget, user rating, and more

All of these work together to make the recommendation process more personalized and engaging for users.

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UI Demo

If you're curious to see how it works visually, here’s a quick demo of the system in action:

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What I Learned as a Beginner

• Building recommendation systems isn’t just about algorithms — it involves handling messy data, user experience design, and testing

• Even simple models like TF-IDF and KNN can be powerful when combined effectively

• Small features (like watchlists and mood filters) significantly improve user engagement

• Real-world projects force you to balance technical goals with practical limitations

It was also a great experience working as a team, dividing tasks based on strengths, and learning from different parts of the project.

Next Steps

While the system works well, we plan to:

✅ Integrate external APIs for more detailed movie information
✅ Explore sentiment-based recommendations
✅ Improve the mood-based filtering logic
✅ Prepare the system for public deployment

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Final Thoughts

This project helped me connect machine learning concepts to an actual product — one that solves a real problem, and reflects how hybrid systems can be more effective than relying on a single method.

It was a stepping stone for me in the world of Machine Learning. Though it’s a beginner project, but working through each challenge made the learning process much more impactful than just following tutorials.

If you’re interested in building recommendation systems or learning how hybrid approaches work, feel free to connect. More details on the project will be uploaded soon.

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GitHub Repo: Pranami Mitra
LinkedIn: Pranami Mitra