Skip to content

The Movielens 100K dataset. The dataset contains of around 1lac ratings given to about 9066 movies by around 671 users. I have implemented Collaborative Filtering using Matrix Factorization with Keras Embeddings to predict the unknown ratings. Also I have used a NN to make predictions. Finally I have tried to minimize the Mean squared Error on t…

License

Notifications You must be signed in to change notification settings

ritumehrotra/Collaborative-Filtering-using-Matrix-Factorization-Neural-Network

Repository files navigation

/*

Author:- Raj Mehrotra
Date:- 06-10-2018

*/

The Movielens 100K dataset. The dataset contains of around 1lac ratings given to about 9066 movies by around 671 users.

I have implemented Collaborative Filtering using Matrix Factorization with Keras Embeddings to predict the unknown ratings. Also I have used a NN to make predictions.

Finally I have tried to minimize the Mean squared Error on the training set. I have achieved a decent validation loss of 0.84.

About

The Movielens 100K dataset. The dataset contains of around 1lac ratings given to about 9066 movies by around 671 users. I have implemented Collaborative Filtering using Matrix Factorization with Keras Embeddings to predict the unknown ratings. Also I have used a NN to make predictions. Finally I have tried to minimize the Mean squared Error on t…

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published