All Projects β†’ Chinmayrane16 β†’ Recommender-Systems-with-Collaborative-Filtering-and-Deep-Learning-Techniques

Chinmayrane16 / Recommender-Systems-with-Collaborative-Filtering-and-Deep-Learning-Techniques

Licence: MIT License
Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques

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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.

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