Recommender-Systems-with-CF-and-DL-Techniques

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.