Ankan SahaBuilding a Movie Recommendation Engine: From Data to Discovery 🍿
Ever wondered how streaming services know exactly what movie you'll love next? It's all thanks to recommendation engines, and I've been diving deep into building one!
This project has been an incredible journey into the world of data science and machine learning. From gathering and cleaning vast datasets to experimenting with different algorithms, it's been a fascinating process.
I'm excited to share some of the key takeaways:
- The power of collaborative filtering: This technique leverages user-item interactions to predict preferences. It's surprisingly effective in finding similar movies, even if they have different genres.
- Content-based filtering: Analyzing movie features like genre, actors, and director can also generate recommendations. This approach works well when exploring new categories.
- Building a robust evaluation framework: Measuring the accuracy and diversity of recommendations is crucial to ensure a satisfying user experience.
The journey is ongoing, but I'm learning a ton about data analysis, model selection, and the impact of technology on our entertainment choices.
What are your favorite movie recommendation features? 🤔 I'd love to hear your thoughts!
