Content Based Tensorflow

TensorFlow Implementation of Content-Based Filtering

Neural Network Architecture Implementation

The content-based filtering system uses a user network and a movie network, implemented very similarly to how we have previously implemented neural networks with dense layers.

User Network Implementation

# User Network - Sequential Model
user_NN = tf.keras.models.Sequential([
  tf.keras.layers.Dense(256, activation='relu'),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(32)  # Final layer outputs 32 numbers
])

The user network:

Uses a sequential model
Has two dense hidden layers with specified number of units
Final layer has 32 units and outputs 32 numbers (v_u)
Hidden layers use ReLU activation function

Movie Network Implementation

# Movie/Item Network - Sequential Model
item_NN = tf.keras.models.Sequential([
  tf.keras.layers.Dense(256, activation='relu'),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(32)  # Final layer outputs 32 numbers
])

The movie network:

Also uses sequential model with dense hidden layers
Final layer outputs 32 numbers (v_m)
Same structure as user network in this example

Input Processing and Vector Computation

User Features Processing

# Extract user input features and compute user vector
input_user = tf.keras.layers.Input(shape=(num_user_features))
vu = user_NN(input_user)
vu = tf.linalg.l2_normalize(vu, axis=1)  # Normalize to length 1

This code:

Extracts input features for the user
Feeds them to the user network defined above to compute v_u
Normalizes the vector v_u to have length one using L2 normalization

Movie Features Processing

# Extract item input features and compute movie vector
input_item = tf.keras.layers.Input(shape=(num_item_features))
vm = item_NN(input_item)
vm = tf.linalg.l2_normalize(vm, axis=1)  # Normalize to length 1

This code:

Extracts item/movie features
Feeds them to the item neural network
Computes the movie vector v_m
Normalizes that vector to have length one

Dot Product Computation

Keras Dot Layer

# Compute dot product between user and movie vectors
output = tf.keras.layers.Dot(axes=1)([vu, vm])

After computing v_u and v_m, we take the dot product using:

Keras special layer type: tf.keras.layers.Dot
Takes dot product between the vectors v_u and v_m
Gives the output of the neural network (final prediction)

Model Definition

Complete Model Structure

# Define overall model with inputs and outputs
model = tf.keras.Model([input_user, input_item], output)

This tells Keras:

The overall model inputs are the user features and movie/item features
The output is the dot product result defined above
Creates a unified model from the two separate networks

Training Configuration

Cost Function and Optimizer

# Configure training
cost_fn = tf.keras.losses.MeanSquaredError()
opt = keras.optimizers.Adam(learning_rate=0.01)
model.compile(optimizer=opt, loss=cost_fn)

The cost function used to train this model is the mean squared error cost function, which is very similar to the collaborative filtering approach.

Key Implementation Features

Vector Normalization

L2 Normalization

The L2 normalization step that normalizes the length of vectors v_u and v_m makes the algorithm work better. TensorFlow has the l2_normalize function that normalizes the vector, also called normalizing the L2 norm of the vector.

Unified Architecture

The implementation demonstrates how to:

Take multiple neural networks (user and movie networks)
Put them together to work in concert
Build a larger, more complex system
Use the inner product of outputs for final predictions

Training Process

All parameters of both user and movie networks are trained simultaneously
No separate training procedure for individual networks
Single cost function optimizes the entire combined system
Networks are judged on how well v_u and v_m predict ratings

Code Integration

These code snippets represent the key components for implementing content-based filtering as a neural network. The complete implementation includes:

Data preprocessing and feature engineering
Model training with the defined architecture
Prediction generation for new users and items
Similar item finding using the learned vectors

The TensorFlow implementation provides a flexible framework for building content-based recommender systems that can incorporate rich user and item features while maintaining computational efficiency and scalability.