CFModel.py

# A simple implementation of matrix factorization for collaborative filtering expressed as a Keras Sequential model

# Keras uses TensorFlow tensor library as the backend system to do the heavy compiling

import numpy as np
from keras.layers import Embedding, Reshape, Merge
from keras.models import Sequential

class CFModel(Sequential):

    # The constructor for the class
    def __init__(self, n_users, m_items, k_factors, **kwargs):
        # P is the embedding layer that creates an User by latent factors matrix.
        # If the intput is a user_id, P returns the latent factor vector for that user.
        P = Sequential()
        P.add(Embedding(n_users, k_factors, input_length=1))
        P.add(Reshape((k_factors,)))

        # Q is the embedding layer that creates a Movie by latent factors matrix.
        # If the input is a movie_id, Q returns the latent factor vector for that movie.
        Q = Sequential()
        Q.add(Embedding(m_items, k_factors, input_length=1))
        Q.add(Reshape((k_factors,)))

        super(CFModel, self).__init__(**kwargs)
        
        # The Merge layer takes the dot product of user and movie latent factor vectors to return the corresponding rating.
        self.add(Merge([P, Q], mode='dot', dot_axes=1))

    # The rate function to predict user's rating of unrated items
    def rate(self, user_id, item_id):
        return self.predict([np.array([user_id]), np.array([item_id])])[0][0]