Recommender Systems:

• Collaborative Filtering with Neural Networks: Implementing a recommendation system using matrix factorization or deep neural networks to predict user preferences.

pythonCopy codefrom tensorflow.keras import layers, models

# Define collaborative filtering model
user_input = layers.Input(shape=(1,))
item_input = layers.Input(shape=(1,))

user_embedding = layers.Embedding(input_dim=num_users, output_dim=50)(user_input)
item_embedding = layers.Embedding(input_dim=num_items, output_dim=50)(item_input)

dot_product = layers.Dot(axes=1)([user_embedding, item_embedding])

model = models.Model([user_input, item_input], dot_product)
model.compile(optimizer='adam', loss='mean_squared_error')

# Train on user-item interaction data
model.fit([user_ids, item_ids], ratings, epochs=10, batch_size=32)