In this repository, I have covered following topics -
- What are Recommendations Systems?
- Why do we need Recommendation Systems?
- Collaborative Filtering
- Types of Collaborative Filtering
- Memory Based CF
- User-Based CF
- Item-Based CF
- Model Based CF
- K-Nearest Neighbours
- Singular Value Decomposition
- Non-Negative Matrix Factorization
- Matrix Factorization using Deep Learning
- Introduction to Embedding Layer
- Architecture 1 with dot operation
- Architecture 2 with concatenation operation
- Evaluating RMSE
- References
You can find the kernel on Kaggle too - Recommender Systems with CF and DL Techniques
I have used Movielens 100k ratings dataset to study about various Recommendation Techniques. Since the dataset size is small, I have used basic techniques but with more size we need to use hybrid and dimensionality reduction techniques.
I have covered one such recommendation technique using autoencoder in another repository (here). This was the second best recommendation technique, released by NVIDIA in 2017 - Training Deep AutoEncoders for Collaborative Filtering.
