Neural Collaborative Filtering is a cutting-edge approach that leverages deep learning to model user-item interactions, uncovering intricate patterns through neural networks. Typically, this method involves feeding user and item embeddings into a Multi-Layer Perceptron (MLP).
Example: Implementing Neural Collaborative Filtering using Keras
python
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from tensorflow.keras.layers import Input, Embedding, Flatten, Dense, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
# Define number of users, items, and embedding dimension
n_users = 100
n_items = 100
embedding_dim = 50
# User input and embedding
user_input = Input(shape=(1,))
user_embedding = Embedding(n_users, embedding_dim)(user_input)
user_embedding = Flatten()(user_embedding)
# Item input and embedding
item_input = Input(shape=(1,))
item_embedding = Embedding(n_items, embedding_dim)(item_input)
item_embedding = Flatten()(item_embedding)
# Concatenate embeddings and pass through dense layers
concat = Concatenate()([user_embedding, item_embedding])
dense1 = Dense(128, activation='relu')(concat)
dense2 = Dense(64, activation='relu')(dense1)
output = Dense(1, activation='sigmoid')(dense2)
# Define and compile the model
model = Model([user_input, item_input], output)…