keras-movielens-cf's Introduction

keras-movielens-cf

A set of Jupyter notebooks demonstrating a simple Keras implmentation of matrix factorization for collaborative filtering. The notebooks use the MovieLens 1M Dataset [1] to show the effectiveness of the architecture. Using 120-dimensional embeddings for users and movies, we achieve an RMSE of 0.862 on a held-out validation set after 15 epochs, taking under 18 minutes on an AWS EC2 g2.2xlarge instance with an NVIDIA GRID K520 GPU.

The notebooks provide the following workflows:

MovieLens 1M ETL: loads and processes user, movie and ratings data to prepare them for input into the Keras model.
MovieLens Training: trains an instance of CFModel using the prepared MovieLens data.
MovieLens Recommendations: shows recommendations generated using the trained model for a given test user.

Requirements

Python 2.7
A copy of the MovieLens 1M dataset, downloaded from [2].

Dependencies

pandas (0.18.1)
matplotlib (1.5.2)
keras (1.0.5)
numpy (1.11.1)
h5py (2.6.0)

License

MIT. See the LICENSE file for the copyright notice.

References

[1] F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (December 2015).

[2] MovieLens 1M Dataset. http://grouplens.org/datasets/movielens/1m/. Last downloaded 2016-08-14.

keras-movielens-cf's Issues

Item to item Recoomandation

I see your code. I found a function rate in CFModel that take user id or item id to predict output. So i need another function for item to item that take item id , item id2 and we produce item to item .

return self.predict([np.array([item_id]), np.array([item_id2])])[0][0]

Am i right ?

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