Machine Learning in Action

Cluster Analysis for Movie Recommendations

👥 User Viewing Data
Chelsie (22)
Loves: Romantic comedies, The Office, Friends
Recently watched: Bridgerton, To All The Boys
Anica (25)
Loves: Action, Marvel movies, Sci-fi
Recently watched: Stranger Things, The Witcher
Brian (28)
Loves: Rom-coms, Drama series, Reality TV
Recently watched: Emily in Paris, The Crown
Biju (30)
Loves: Action, Thrillers, Crime documentaries
Recently watched: Ozark, Breaking Bad
Tahsin (24)
Loves: Romance, Comedy, Feel-good movies
Recently watched: Never Have I Ever, Heartstopper
🧠 ML Clustering Algorithm
📱 Romance/Comedy Cluster
Chelsie
Brian
Tahsin
⚡ Action/Thriller Cluster
Anica
Biju
Algorithm groups users with similar viewing patterns
🎯 New User: Lisa
Lisa (23) - New User
Just watched: The Notebook, Friends
Algorithm detected: Romance/Comedy preferences
🍿 Recommendations
Bridgerton
Because Sarah & Emma loved it
To All The Boys
Popular in your cluster
Emily in Paris
Similar users enjoyed this
Never Have I Ever
Trending in Romance/Comedy
How Cluster-Based Recommendations Work
1
Collect viewing history data from all users including ratings, watch time, and genre preferences
2
Group users with similar viewing patterns into clusters using machine learning algorithms
3
When a new user joins, identify which cluster they belong to based on initial activity
4
Recommend popular content from that cluster that the new user hasn't seen yet