🔴 Building a Neural Collaborative Filtering Recommendation System

[stemaway]

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 or pip install torch)
• 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 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:

• 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.