📸 Advanced Instagram Recommendation Algorithm Simulator
Explore the complex mechanics behind social media content ranking
Multi-Signal Content Scoring System
0 20
0 10
0 5
0 3
0 15
👤 Creator Tier
0.1 48
0 1
From Generic → Personalized Content
0 100
👥 Demographics
🌍 Region
0 0.5
See Your Personalized Feed in Action
Deep Dive into Algorithm Mechanics
How This Simulation Works:
- Value Model: Converts user engagement signals into probability scores using sigmoid functions
- Cold Start: Blends global trends with personal preferences based on interaction history
- Embeddings: Represents users and content in high-dimensional vector space
- Ranking: Combines relevance scores with diversity penalties for balanced feeds
- Personalization: Gradually shifts from trending to personalized content
Key Concepts:
- Attention Mechanism: Weighted content selection based on user interests
- Temporal Decay: Newer content gets priority boost
- Diversity Penalty: Prevents echo chambers by promoting content variety
- Demographic Targeting: Adjusts recommendations based on user demographics
- Exploration vs Exploitation: Balance between showing familiar and new content
Advanced Features:
- 8-Dimensional Embedding Space for richer content representation
- Multi-Signal Value Model with 5 engagement types
- Demographic & Regional Biases in recommendation
- Dynamic Exploration Factor for content discovery
- Attention-Based Ranking with diversity constraints
- Temporal Content Decay for freshness prioritization
- Creator Tier Influence on engagement predictions