Created
March 20, 2021 13:21
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GMF implementation inspired by https://github.com/hexiangnan/neural_collaborative_filtering/blob/master/GMF.py
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regs = regs or [0, 0] | |
user_input = Input(shape=(1,), dtype="int32", name="user_input") | |
item_input = Input(shape=(1,), dtype="int32", name="item_input") | |
user_embeds = Embedding( | |
input_dim=num_users, | |
output_dim=embed_size, | |
embeddings_initializer="normal", | |
embeddings_regularizer=l2(regs[0]), | |
input_length=1, | |
) | |
item_embeds = Embedding( | |
input_dim=num_items, | |
output_dim=embed_size, | |
embeddings_initializer="normal", | |
embeddings_regularizer=l2(regs[1]), | |
input_length=1, | |
) | |
user_flattened = Flatten()(user_embeds(user_input)) | |
item_flattened = Flatten()(item_embeds(item_input)) | |
predict_vector = multiply([user_flattened, item_flattened]) | |
prediction = Dense( | |
1, | |
activation="sigmoid", | |
kernel_initializer="lecun_uniform", | |
name="prediction", | |
)(predict_vector) | |
model = Model(inputs=[user_input, item_input], outputs=prediction) |
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