Rate movies, find users with similar taste, and watch the algorithm recommend films you've never seen — exactly how Netflix, Spotify, and YouTube work inside!
⭐ Rate Items
👥 Find Similar Users
🎯 Get Recommendations
🗂️ Rating Matrix
🏆 Badge
How Collaborative Filtering Works
⭐
Collect Ratings
Users rate items (1–5 stars). These ratings form a giant matrix — rows are users, columns are items.
👥
Find Similar Users
Compare your ratings to everyone else's using cosine similarity. High similarity = shared taste.
🔍
Fill the Gaps
Your "taste twins" rated items you haven't seen. Predict your rating based on their scores.
🎯
Recommend Top Items
Show the highest predicted-rating items you haven't rated yet. That's your personalised recommendation!
🎬
Wizzy the AI Tutor
Welcome to the Recommendation Lab! 🎉 Rate at least 6 movies by clicking the stars. Your ratings will be compared to other users to find your "taste twins" — people who like exactly what you like. Rate honestly — the recommendations depend on it!
Step 1 — Rate These Movies
0 / 12 rated
⭐ Your Ratings
Rated0
Avg rating—
Highest rated—
Lowest rated—
💡 Rating guide:
⭐ = Hated it ⭐⭐ = Didn't like ⭐⭐⭐ = It was OK ⭐⭐⭐⭐ = Liked it ⭐⭐⭐⭐⭐ = Loved it!
Leave items blank if you haven't seen them.
🎬
Wizzy the AI Tutor
Look at your taste twins! 👥 We compare your ratings to 8 other users using cosine similarity — a maths formula that measures how aligned two rating vectors are. A score of 1.0 = perfect match, 0 = completely different taste!
Step 2 — User Similarity Scores
📊 Similarity Stats
Most similar—
Similarity score—
Least similar—
Your ratings used—
Cosine Similarity:
Treats each user's ratings as a vector in N-dimensional space. If two vectors point in the same direction, their cosine similarity is close to 1!
🎬
Wizzy the AI Tutor
Here are your personalised recommendations! 🎯 These are movies your taste twins loved but you haven't rated yet. The predicted score is a weighted average of their ratings — users who are more similar to you have more influence. Does this match what you'd actually enjoy?
Step 3 — Your Personalised Recommendations
🎬
Wizzy the AI Tutor
This is the rating matrix — the data structure behind every recommender system! Rows are users, columns are movies. Empty cells are what the algorithm tries to predict. Notice how sparse it is — most users have only rated a few items. That's called the sparsity problem!
Step 4 — The Rating Matrix
⬜ Not rated⭐ 1⭐⭐ 2⭐⭐⭐ 3⭐⭐⭐⭐ 4⭐⭐⭐⭐⭐ 5
🎬
Wizzy the AI Tutor
🎊 You've just built the algorithm behind Netflix, Spotify, Amazon, and YouTube! Collaborative filtering powers billions of recommendations every day. You understand user-based CF, cosine similarity, and the sparsity problem. That's real ML engineering!
🎬
Recommender Badge!
You built a collaborative filtering recommendation engine from scratch!
Optional. Stays on this device only — not sent to WhizzStep.
🎬 WhizzStep AI Lab
Activity completion card for
Student Name
has built a Collaborative Filtering Recommendation Engine
This records completion of a browser activity only. It is not an accredited certificate or proof of mastery.
Recommender Expert
Cosine Similarity
Netflix Engineer
whizzstep.in
Key Concepts Mastered
Collaborative Filtering
👥 Taste Twins
"Users who liked what you liked also liked X." No content analysis needed — just patterns in ratings.
Cosine Similarity
📐 Vector Alignment
Treats ratings as vectors. cos(θ) between two vectors = similarity. 1.0 = same direction = same taste.
Sparsity Problem
🕳️ Missing Data
Most users rate very few items — the matrix is 99% empty. Matrix factorisation (SVD) solves this.
Cold Start
🥶 New User Problem
A brand new user has no ratings — can't find taste twins. Fix: ask for preferences at sign-up.
Filter Bubble
🫧 Echo Chamber
Recommenders only show what you already like, narrowing your experience. A real ethical concern!
Content-Based
🎭 The Other Approach
Instead of user similarity, analyse item features (genre, director). Netflix uses both methods together.
About this lab
AI-14How AI and Machine Learning WorkClasses 8-1015 minApplied
Learning objective: Explore a simplified collaborative-filtering demonstration and see how shared preferences drive suggestions.
What this simplifies: This is a small illustrative model, not the system used by any specific commercial platform.
Privacy: No learner input leaves the device.
Teacher prompt: Ask the class why this simulation might mislead someone who takes it too literally.
Reflect: What is one thing this activity showed you that you did not expect?
