Recommendation AI Simulator 🎯

🤝 Collaborative Filtering

📊 Content-Based

🔀 Hybrid Demo

📈 A/B Testing

🆕 Cold Start Handler

🤝 Collaborative Filtering

Find patterns in user behavior to recommend items based on what similar users liked.

User-Item Rating Matrix

Sample ratings (1-5 stars) from users for movies

Similarity Calculation

Select Target User:

📊 Content-Based Filtering

Recommend items based on features and user preferences for specific attributes.

User Profile

Build a preference profile based on liked items

Item Features

Movies with their feature vectors

Similarity Method

🔀 Hybrid Recommendation System

Combine multiple algorithms for better recommendations using weighted ensembles.

Algorithm Weights

Collaborative Filtering Weight: 50% Content-Based Weight: 30% Popularity Weight: 20%

📈 A/B Testing Framework

Compare recommendation algorithms and measure their effectiveness.

Test Configuration

Algorithm A:

Algorithm B:

Sample Size: 1000 users

🆕 Cold Start Problem Handler

Strategies for recommending to new users or recommending new items without historical data.

Scenario Selection

Available Strategies

📊 Popularity-Based

Recommend trending items

❓ Questionnaire

Ask user preferences

🎯 Demographic

Use demographic info

🔍 Content Features

Analyze item metadata

🌐 Social

Use social connections

🎲 Exploration

Multi-armed bandit

🎯 Recommendation AI Simulator

🤝 Collaborative Filtering

User-Item Rating Matrix

Similarity Calculation

Recommendations

📊 Content-Based Filtering

User Profile

Item Features

Similarity Method

Recommended Items

🔀 Hybrid Recommendation System

Algorithm Weights

Hybrid Results

📈 A/B Testing Framework

Test Configuration

Test Results

Algorithm A

Algorithm B

Statistical Significance

🆕 Cold Start Problem Handler

Scenario Selection

Available Strategies

📊 Popularity-Based

❓ Questionnaire

🎯 Demographic

🔍 Content Features

🌐 Social

🎲 Exploration

Cold Start Recommendations