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[Storyboard] Collaborative Filtering vs Content-Based Filtering #106

@divyansheeverma

Description

@divyansheeverma

Concept name

Recommendation Systems : Collaborative Filtering vs Content-Based Filtering

What is this concept?

This storyboard explains the two most common recommendation system techniques used in machine learning: Collaborative Filtering and Content-Based Filtering. It visually demonstrates how platforms like Netflix, YouTube, Spotify, and Amazon recommend movies, songs, videos, or products to users.

Why does it matter?

Recommendation systems are one of the most widely used real-world applications of AI. Beginners interact with them every day but rarely understand how they work behind the scenes. This storyboard helps learners intuitively understand how AI personalizes recommendations based on user behaviour and item similarity.

Target audience

  • Complete beginner (no CS background)
  • Curious explorer (knows some Python)
  • Builder (comfortable with ML basics)

Key ideas to visualise

  1. User watching/interacting with content
  2. Collaborative filtering:
  •  Similar users liking similar items
    
  •  User-user similarity
    
  •  Matrix-style visualization
    
  1. Content-based filtering:
  •  Item features/tags/categories
    
  •  Similarity between items
    
  1. Comparison between the two approaches
  2. Advantages and limitations of each method
  3. Real-world examples from Netflix, Spotify, YouTube, and Amazon

References / inspiration

Netflix recommendation system blogs

Spotify Recommendation system

Google ML recommendation systems guide

Can you create this storyboard?

  • Yes, I'd like to illustrate this
  • No, I'm just proposing the idea

@escher-m I would like to work on this issue under GSSoC'26. Kindly assign it to me.

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