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Recommendation Systems Explained
Explaining & Implementing Content Based, Collaborative Filtering & Hybrid Recommendation Systems in Python
Table of Contents
- What is a Recommendation System
- What Defines a Good Recommendation
- K Fold Cross Validation
- MSE
- RMSD - Data
- Requirements
- Code - Collaborative Filtering Recommendation System
- Intuition
- Advantages
- Disadvantages
- Example
- Implementation - Content Based Recommendation System
- Intuition
- Advantages
- Disadvantages
- Example
- Implementation - Hybrid Recommendation System
- Intuition
- Advantages
- Disadvantages
- Example
- Implementation - Concluding Remarks
- Resources
What is a Recommendation System
Recommendation engines are a subclass of machine learning which generally deal with ranking or rating products / users. Loosely defined, a recommender system is a system which predicts ratings a user might give to a specific item. These predictions will then be ranked and returned back to the user.
They’re used by various large name companies like Google, Instagram, Spotify, Amazon, Reddit, Netflix…