🟡 Building a Recommendation System with Unsupervised Autoencoders

AIVIA Project Engine Recommender & Decision Systems
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Building a Recommendation System with Unsupervised Autoencoders

Objective

Develop a movie recommendation system using unsupervised autoencoders to learn latent representations of user preferences and item characteristics. This project focuses on applying deep learning techniques to perform collaborative filtering by reconstructing user-item interaction data. You will work with the MovieLens dataset to build, train, and evaluate an autoencoder model, gaining hands-on experience with unsupervised learning in recommendation systems.

Learning Outcomes

By completing this project, you will:

  • Understand the concept of autoencoders and their application in recommendation systems.
  • Implement an unsupervised autoencoder model using deep learning frameworks like TensorFlow or PyTorch.
  • Handle and preprocess large-scale datasets, preparing them for neural network training.
  • Learn to reconstruct user-item interaction matrices to capture latent factors.
  • Evaluate the recommendation performance using appropriate metrics.
  • Gain experience in optimizing unsupervised models for better performance.

Prerequisites and Theoretical Foundations

1. Python Programming (Intermediate Level)

  • Data Structures: Lists, dictionaries, sets, and tuples.
  • Control Flow: Loops, conditionals, and functions.
  • Object-Oriented Programming: Classes and inheritance.
  • Libraries: Familiarity with Pandas, NumPy, and Matplotlib.

2. Basic Machine Learning Concepts

  • Unsupervised Learning:
    • Understanding of clustering and dimensionality reduction.
  • Neural Networks:
    • Basic knowledge of neural network architectures, activation functions, and training processes.
  • Autoencoders:
    • Understanding of encoder-decoder structures and reconstruction loss.

3. Introduction to Recommender Systems

  • Collaborative Filtering:
    • User-based and item-based collaborative filtering.
  • Matrix Factorization:
    • Concept of decomposing the interaction matrix into latent factors.
  • Data Sparsity and Cold Start Problems:
    • Challenges in recommendation systems and how autoencoders can help.

Skills Gained

  • Data Preprocessing: Handling missing values and preparing data for unsupervised learning.
  • Model Implementation: Building and training an autoencoder for reconstructing interaction data.
  • Deep Learning Frameworks: Practical experience with TensorFlow or PyTorch for unsupervised models.
  • Dimensionality Reduction: Learning to capture essential information in lower-dimensional spaces.
  • Model Evaluation: Using metrics specific to recommendation systems to assess model performance.
  • Optimization Techniques: Applying methods to improve the performance of unsupervised models.

Tools Required

  • Programming Language: Python 3.7+
  • Libraries and Frameworks:
    • Pandas: Data manipulation (pip install pandas)
    • NumPy: Numerical computations (pip install numpy)
    • Matplotlib: Visualization (pip install matplotlib)
    • Scikit-learn: Machine learning utilities (pip install scikit-learn)
    • TensorFlow or PyTorch: Deep learning framework (pip install tensorflow or pip install torch)
  • Dataset:
  • Environment:
    • Jupyter Notebook or an IDE like PyCharm or VSCode

Project Structure

autoencoder_recommender/
│
├── data/
│   └── movielens/
│       ├── ratings.csv
│       └── movies.csv
│
├── src/
│   ├── data_preprocessing.py
│   ├── autoencoder_model.py
│   ├── train.py
│   ├── evaluate.py
│   └── utils.py
│
└── notebooks/
    └── autoencoder_recommender.ipynb

Steps and Tasks

1. Data Acquisition and Exploration

Tasks:

  • Download the MovieLens Dataset:
    • Choose the 100K or 1M dataset for manageability.
  • Load Data into Pandas DataFrames:
    • Read ratings.csv and movies.csv.
  • Perform Exploratory Data Analysis (EDA):
    • Understand the distribution of ratings.
    • Analyze the number of unique users and movies.

Implementation:

import pandas as pd
import matplotlib.pyplot as plt

# Load data
ratings = pd.read_csv('data/movielens/ratings.csv')
movies = pd.read_csv('data/movielens/movies.csv')

# EDA
print(ratings.head())
print(f"Number of users: {ratings['userId'].nunique()}")
print(f"Number of movies: {ratings['movieId'].nunique()}")

# Rating distribution
plt.hist(ratings['rating'], bins=5)
plt.xlabel('Rating')
plt.ylabel('Count')
plt.title('Rating Distribution')
plt.show()

2. Data Preprocessing

Tasks:

  • Create User-Item Interaction Matrix:
    • Pivot the ratings DataFrame to create a matrix.
  • Handle Missing Values:
    • Fill missing values with zeros (assuming unrated).
  • Normalize Ratings (Optional):
    • Center ratings around the mean for each user.

Implementation:

import numpy as np

# Create user-item interaction matrix
user_item_matrix = ratings.pivot(index='userId', columns='movieId', values='rating').fillna(0)
user_item_matrix = user_item_matrix.astype(np.float32)

# Convert DataFrame to NumPy array
interaction_matrix = user_item_matrix.values

# Print the shape of the interaction matrix
print(f"Interaction matrix shape: {interaction_matrix.shape}")

3. Implementing the Autoencoder Model

Tasks:

  • Define the Autoencoder Architecture:
    • Create an encoder that compresses the input.
    • Create a decoder that reconstructs the input from the compressed representation.
  • Choose Activation Functions and Loss Function:
    • Use activation functions like ReLU and sigmoid.
    • Use Mean Squared Error (MSE) as the loss function.

Implementation (using PyTorch):

import torch
import torch.nn as nn

class Autoencoder(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super(Autoencoder, self).__init__()
        # Encoder
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU()
        )
        # Decoder
        self.decoder = nn.Sequential(
            nn.Linear(hidden_dim, input_dim),
            nn.Sigmoid()
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

# Instantiate the model
input_dim = interaction_matrix.shape[1]  # Number of movies
hidden_dim = 64  # You can adjust this value
model = Autoencoder(input_dim, hidden_dim)

4. Preparing the Training Loop

Tasks:

  • Convert Data to Torch Tensors:
    • Prepare the interaction matrix as input for the model.
  • Define Loss Function and Optimizer:
    • Use MSELoss and an optimizer like Adam.
  • Create DataLoader for Batching:
    • Use PyTorch’s DataLoader to handle batching.

Implementation:

import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# Convert interaction matrix to Torch tensor
interaction_tensor = torch.tensor(interaction_matrix)

# Create TensorDataset and DataLoader
dataset = TensorDataset(interaction_tensor)
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

5. Training the Model

Tasks:

  • Implement the Training Loop:
    • Iterate over epochs and batches.
    • Perform forward and backward propagation.
  • Monitor Training Loss:
    • Print or log the loss at intervals.

Implementation:

num_epochs = 20

for epoch in range(num_epochs):
    model.train()
    total_loss = 0
    for batch in data_loader:
        # Get batch data
        batch_input = batch[0]
        optimizer.zero_grad()
        # Forward pass
        outputs = model(batch_input)
        loss = criterion(outputs, batch_input)
        # Backward pass and optimization
        loss.backward()
        optimizer.step()
        total_loss += loss.item()

    avg_loss = total_loss / len(data_loader)
    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss:.4f}")

6. Generating Recommendations

Tasks:

  • Obtain the Encoded Representations:
    • Use the encoder part of the model to get user embeddings.
  • Compute Predicted Ratings:
    • Use the full model to predict ratings for all items.
  • Generate Top-N Recommendations:
    • For each user, recommend items with the highest predicted ratings that the user hasn’t interacted with yet.

Implementation:

model.eval()
with torch.no_grad():
    # Get reconstructed ratings
    reconstructed = model(interaction_tensor)
    reconstructed = reconstructed.numpy()

# Generate recommendations for a specific user
user_id = 0  # Change as needed
user_ratings = reconstructed[user_id]
# Get already interacted items
interacted_items = interaction_matrix[user_id].nonzero()[0]
# Exclude already interacted items
user_ratings[interacted_items] = -np.inf
# Get top-N recommendations
top_N = 10
recommended_items = np.argsort(-user_ratings)[:top_N]
print(f"Top {top_N} recommended items for user {user_id}: {recommended_items}")

7. Evaluating the Model

Tasks:

  • Split Data into Training and Testing Sets:
    • Hold out some interactions for testing.
  • Define Evaluation Metrics:
    • Use metrics like Recall@K, Precision@K, and NDCG@K.
  • Implement Evaluation Function:
    • Assess the quality of recommendations.

Implementation:

from sklearn.model_selection import train_test_split

# Flatten the interaction matrix
user_item_pairs = [(u, i) for u in range(interaction_matrix.shape[0]) for i in range(interaction_matrix.shape[1])]
ratings = interaction_matrix.flatten()

# Split into training and testing
train_pairs, test_pairs, train_ratings, test_ratings = train_test_split(
    user_item_pairs, ratings, test_size=0.2, random_state=42)

# Reconstruct interaction matrices
train_matrix = np.zeros_like(interaction_matrix)
for (u, i), r in zip(train_pairs, train_ratings):
    train_matrix[u, i] = r

test_matrix = np.zeros_like(interaction_matrix)
for (u, i), r in zip(test_pairs, test_ratings):
    test_matrix[u, i] = r

# Re-train the model on training data
# (Repeat the training steps with train_matrix)

# Evaluate the model
def evaluate(model, test_matrix, top_k=10):
    model.eval()
    with torch.no_grad():
        reconstructed = model(torch.tensor(train_matrix))
        reconstructed = reconstructed.numpy()
    hits = 0
    total = 0
    for user_id in range(test_matrix.shape[0]):
        user_ratings = reconstructed[user_id]
        interacted_items = train_matrix[user_id].nonzero()[0]
        user_ratings[interacted_items] = -np.inf
        recommended_items = np.argsort(-user_ratings)[:top_k]
        test_items = test_matrix[user_id].nonzero()[0]
        hits += len(set(recommended_items) & set(test_items))
        total += len(test_items)
    recall = hits / total if total > 0 else 0
    print(f"Recall@{top_k}: {recall:.4f}")

# Call the evaluation function
evaluate(model, test_matrix)

8. Hyperparameter Tuning

Tasks:

  • Experiment with Different Hidden Dimensions:
    • Try different sizes like 32, 64, 128.
  • Adjust Learning Rate and Batch Size:
    • Observe the impact on training convergence.
  • Implement Regularization Techniques:
    • Use dropout or L2 regularization to prevent overfitting.

Implementation:

# Example of trying different hidden dimensions
for hidden_dim in [32, 64, 128]:
    model = Autoencoder(input_dim, hidden_dim)
    # Repeat the training and evaluation steps
    # Compare the results to find the optimal hidden dimension

9. Conclusion and Next Steps

Tasks:

  • Summarize Findings:
    • Discuss how well the autoencoder performed in reconstructing the interaction matrix.
  • Identify Potential Improvements:
    • Suggest methods like stacking autoencoders or incorporating content-based features.
  • Explore Advanced Techniques:
    • Consider using Variational Autoencoders (VAEs) or Denoising Autoencoders.