Building a Neural Collaborative Filtering Recommendation System
Objective
Develop a movie recommendation system by implementing Neural Collaborative Filtering (NCF) using deep learning techniques. This project focuses on building a recommendation model that predicts user preferences for movies based on historical interaction data. You will work with the MovieLens dataset to train and evaluate your model, gaining hands-on experience with deep learning frameworks and recommender system algorithms.
Learning Outcomes
By completing this project, you will:
- Understand Neural Collaborative Filtering and its application in recommendation systems.
- Implement a deep learning model using frameworks like TensorFlow or PyTorch.
- Handle and preprocess real-world datasets, preparing them for neural network training.
- Evaluate the model’s performance using appropriate metrics and validation techniques.
- Gain experience in hyperparameter tuning to optimize model performance.
- Understand the challenges and solutions associated with recommendation systems.
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
- Supervised Learning:
- Understanding of regression and classification.
- Neural Networks:
- Basic knowledge of neural network architectures, activation functions, and training processes.
- Evaluation Metrics:
- Understanding of accuracy, precision, recall, and loss functions.
3. Introduction to Recommender Systems
- Collaborative Filtering:
- User-based and item-based collaborative filtering.
- Implicit vs. Explicit Feedback:
- Understanding the difference and how to handle each type.
- Matrix Factorization:
- Basic concept of decomposing the interaction matrix.
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
orpip install torch
)
- Pandas: Data manipulation (
- Dataset:
- MovieLens 100K or 1M: Download from the MovieLens website
- Environment:
- Jupyter Notebook or an IDE like PyCharm or VSCode
Project Structure
neural_cf_recommender/
│
├── data/
│ └── movielens/
│ ├── ratings.csv
│ └── movies.csv
│
├── src/
│ ├── data_preprocessing.py
│ ├── model.py
│ ├── train.py
│ ├── evaluate.py
│ └── utils.py
│
└── notebooks/
└── neural_cf_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
andmovies.csv
.
- Read
- 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:
- Encode User IDs and Movie IDs:
- Convert IDs to continuous integers starting from 0.
- Split Data into Training and Testing Sets:
- Use a suitable method to prevent data leakage.
- Prepare Data for Model Input:
- Create tensors or arrays suitable for the deep learning model.
Implementation:
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
# Encode user IDs and movie IDs
user_encoder = LabelEncoder()
movie_encoder = LabelEncoder()
ratings['user'] = user_encoder.fit_transform(ratings['userId'])
ratings['movie'] = movie_encoder.fit_transform(ratings['movieId'])
num_users = ratings['user'].nunique()
num_movies = ratings['movie'].nunique()
print(f"Number of users: {num_users}, Number of movies: {num_movies}")
# Split data
train_data, test_data = train_test_split(ratings, test_size=0.2, random_state=42)
3. Implementing the Neural Collaborative Filtering Model
Tasks:
- Define the Model Architecture:
- Create embedding layers for users and movies.
- Combine embeddings and pass through neural network layers.
- Choose Activation Functions and Output Layer:
- Use appropriate activation functions (e.g., ReLU, sigmoid).
- Compile the Model:
- Select loss function and optimizer.
Implementation (using PyTorch):
import torch
import torch.nn as nn
class NeuralCollaborativeFiltering(nn.Module):
def __init__(self, num_users, num_items, embedding_size):
super(NeuralCollaborativeFiltering, self).__init__()
self.user_embedding = nn.Embedding(num_users, embedding_size)
self.item_embedding = nn.Embedding(num_items, embedding_size)
self.fc_layers = nn.Sequential(
nn.Linear(embedding_size * 2, 64),
nn.ReLU(),
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 1)
)
self.sigmoid = nn.Sigmoid()
def forward(self, user_indices, item_indices):
user_embed = self.user_embedding(user_indices)
item_embed = self.item_embedding(item_indices)
vector = torch.cat([user_embed, item_embed], dim=-1)
logits = self.fc_layers(vector)
rating = self.sigmoid(logits)
return rating
# Instantiate the model
embedding_size = 20
model = NeuralCollaborativeFiltering(num_users, num_movies, embedding_size)
4. Preparing the Training Loop
Tasks:
- Define Loss Function and Optimizer:
- Use binary cross-entropy loss for implicit feedback.
- Implement Negative Sampling:
- Generate negative samples for training.
- Create DataLoader for Batching:
- Utilize PyTorch’s DataLoader for efficient data loading.
Implementation:
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# Define the loss function and optimizer
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Prepare the dataset with negative sampling
class NCFDataset(Dataset):
def __init__(self, data, num_items, num_negatives=4):
self.users = []
self.items = []
self.labels = []
user_item_set = set(zip(data['user'], data['movie']))
all_items = set(range(num_items))
for idx, row in data.iterrows():
u = row['user']
i = row['movie']
self.users.append(u)
self.items.append(i)
self.labels.append(1)
for _ in range(num_negatives):
negative_item = np.random.choice(list(all_items - set([i])))
self.users.append(u)
self.items.append(negative_item)
self.labels.append(0)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return self.users[idx], self.items[idx], self.labels[idx]
# Create DataLoader
train_dataset = NCFDataset(train_data, num_movies)
train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True)
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:
import numpy as np
num_epochs = 5
for epoch in range(num_epochs):
model.train()
total_loss = 0
for user_batch, item_batch, label_batch in train_loader:
user_batch = torch.LongTensor(user_batch)
item_batch = torch.LongTensor(item_batch)
label_batch = torch.FloatTensor(label_batch)
optimizer.zero_grad()
outputs = model(user_batch, item_batch).squeeze()
loss = criterion(outputs, label_batch)
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len(train_loader)
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")
6. Evaluating the Model
Tasks:
- Prepare Test Data:
- Ensure no data leakage from training.
- Choose Evaluation Metrics:
- Use metrics like Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG).
- Implement Evaluation Function:
- Calculate metrics over the test set.
Implementation:
def evaluate_model(model, test_data, top_k=10):
model.eval()
hits, ndcgs = [], []
with torch.no_grad():
for idx, row in test_data.iterrows():
user = torch.LongTensor([row['user']])
item = torch.LongTensor([row['movie']])
label = row['rating']
# Get prediction scores for all items
user_items = torch.LongTensor(np.array(range(num_movies)))
users = user.repeat(num_movies)
outputs = model(users, user_items).squeeze()
_, indices = torch.topk(outputs, top_k)
recommended_items = indices.numpy()
if row['movie'] in recommended_items:
hits.append(1)
rank = np.where(recommended_items == row['movie'])[0][0] + 1
ndcgs.append(1 / np.log2(rank + 1))
else:
hits.append(0)
ndcgs.append(0)
hr = np.mean(hits)
ndcg = np.mean(ndcgs)
print(f"Hit Ratio @ {top_k}: {hr:.4f}, NDCG @ {top_k}: {ndcg:.4f}")
# Evaluate the model
evaluate_model(model, test_data)
7. Hyperparameter Tuning
Tasks:
- Experiment with Different Embedding Sizes:
- Try different sizes like 10, 20, 50.
- Adjust Learning Rate and Batch Size:
- Observe the impact on training convergence.
- Implement Early Stopping:
- Stop training when validation loss doesn’t improve.
Implementation:
# Example of trying different embedding sizes
for embedding_size in [10, 20, 50]:
model = NeuralCollaborativeFiltering(num_users, num_movies, embedding_size)
# Repeat the training and evaluation steps
# Compare the results to find the optimal embedding size
8. Conclusion and Next Steps
Tasks:
- Summarize Findings:
- Discuss model performance and observations.
- Identify Potential Improvements:
- Suggest methods like incorporating content-based features or using more advanced architectures.
Conclusion
In this project, you:
- Developed a neural collaborative filtering model using deep learning techniques.
- Preprocessed and prepared data from the MovieLens dataset.
- Implemented a training loop with negative sampling to handle implicit feedback.
- Evaluated the model’s performance using appropriate metrics.
- Experimented with hyperparameters to optimize the model.
This project provides a solid foundation in building deep learning-based recommendation systems. You can extend this work by:
- Incorporating Content-Based Features:
- Integrate movie metadata like genres or descriptions to improve recommendations.
- Exploring Advanced Architectures:
- Implement models like Autoencoders or Graph Neural Networks.
- Deploying the Model:
- Build an API to serve recommendations in real-time.