Recommender Systems with Graph Neural Networks (GNNs)
Advanced Track  TechDriven AI Explorations
This project has been selected for expansion into 2024 virtualinternships.
Interested? Apply here.
Objective
Develop a recommender system using Graph Neural Networks (GNNs) to predict the next movie recommendation for users based on their historical movie watching and rating behaviors. Utilize the MovieLens dataset and PyTorch Geometric to implement GNN models that can effectively harness the relational data between users and movies. Explore advanced graphbased methods with IGMC for potential expansion in a VirtualInternship setting.
Learning Outcomes
 Master graphbased data analysis and the implementation of GNNs for recommender systems.
 Gain proficiency in using PyTorch Geometric, Pandas, and NetworkX to handle and analyze complex data structures.
 Understand the dynamics of usermovie interactions through graph theory and machine learning techniques.
 Optionally explore advanced GNN functionalities and optimizations through the IGMC framework, enhancing the capability to manage and interpret complex graphbased data models.
 Understand the application of GNNs in industry settings, where recommender systems are crucial for ecommerce, streaming services, and social media platforms.
Prerequisite Skills
 Python programming (Intermediate level)
 Basic understanding of graph theory and neural networks
 Basic understanding of recommender systems (e.g., collaborative filtering) is beneficial
 Familiarity with machine learning frameworks like PyTorch (beneficial for working with PyTorch Geometric and IGMC)
Skills Gained
 GraphBased Data Analysis
 Development of Recommender Systems using GNNs
 Practical application of PyTorch Geometric in realworld datasets
 Advanced model tuning and optimization using IGMC, providing deeper insights into graph neural network capabilities.
 Handson experience with scalability challenges in recommender systems, a critical aspect for realworld applications.
Tools Explored
 PyTorch Geometric
 Pandas for data manipulation
 NetworkX for initial graphbased operations
 Matplotlib or Plotly for visualizing graph structures and model performance metrics
 IGMC for advanced graph neural network implementations and optimizations, demonstrating cuttingedge techniques in graphbased machine learning.
Steps and Tasks
At any point during your project, if you find yourself needing assistance, several resources are available to support you:
 Code Snippets: Code snippets for each step are shared in the next section.
 CodeAlong Discussions Category: Join discussions to exchange ideas and resolve issues.
 STEMAway Mentorship Category: For paid members, access live mentorship, including forum support and webinars, to enhance your learning experience.
1. Setup and Data Preparation

Install Essential Python Libraries: Begin by installing all necessary Python libraries required for data manipulation, graph analysis, and deep learning. Ensure you have libraries such as PyTorch, PyTorch Geometric, Pandas, and NetworkX installed.

Download and Preprocess the MovieLens dataset: Utilize this popular dataset to build and test recommender systems. It offers extensive usermovie interaction data which you will preprocess to identify patterns like historical viewing habits and ratings. Conduct an initial exploratory data analysis to understand the datasetâs structure and identify key features such as user demographics and movie genres. These insights will guide your subsequent graph construction.
Identify and handle missing values, outliers, or noise in the MovieLens dataset. Understanding data quality issues is crucial for building robust recommender systems.
2. Graph Construction and Feature Engineering

Construct a Graph with NetworkX: Represent users and movies as nodes in a graph, with edges reflecting the interactions between them, such as movies watched by users. Assign attributes to these edges based on the movie ratings provided by users, critical for the training of your GNN models.

Engineer Node Features: Augment your graph by adding detailed features to the nodes. For users, incorporate demographic data; for movies, include metadata such as genre, release year, or director. These enhancements create a richer graph representation, improving the efficacy of your recommender system.

Edge Weighting Schemes: Experiment with different edge weighting schemes, such as using raw ratings, normalized ratings, or incorporating temporal information like recency of ratings. This can significantly influence the GNNâs understanding of usermovie interactions.

Convert to a PyTorch Geometric Format: After fully constructing your graph with NetworkX, transform it into a format compatible with PyTorch Geometric. This step ensures efficient computations and takes advantage of PyTorch Geometricâs advanced functionalities for training graph neural networks.

Advanced Option with IGMC: Consider exploring IGMC (Inductive Graphbased Matrix Completion) for more complex scenarios. IGMC provides highperformance tools and specialized techniques ideal for indepth GNN applications, offering an avenue for advanced learners to delve into sophisticated graphbased ML challenges.
3. Implementing GNN Models
Basic GNN Implementation

Model Architecture Design: Begin by defining a basic GNN structure using PyTorch Geometric. Focus on simple architectures that are easy to understand and implement.

Selection of GNN Layers: Use standard GNN layers like Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT). While GCNs are a good starting point, GATs might be more suitable for this task due to their ability to weigh the importance of different usermovie interactions. Empirically validate both approaches to determine which performs better for your specific dataset. These layers are sufficient for basic graph tasks and provide a solid foundation in GNN applications.

Architecture Considerations: Implement a straightforward multilayer GNN setup with common activation functions like ReLU. Ensure the model is simple to configure and understand.

Model Configuration and Setup: Set up your model with a basic optimizer like Adam and a simple loss function like Mean Squared Error. This setup will help you grasp the fundamentals of GNN training.

Model interpretability: Investigate techniques to interpret your GNNâs decisions, such as visualizing node embeddings or analyzing attention weights in GAT. This not only aids in model refinement but also in explaining recommendations to stakeholders or users.
Advanced GNN Implementation with IGMC

Model Architecture Design: Enhance your modelâs structure by incorporating graphlevel dropout techniques inspired by IGMC to improve robustness and mitigate overfitting.

Selection of GNN Layers: For complex data interactions, utilize RGCN layers from IGMC. These layers are designed to handle multiple types of relationships effectively.

Architecture Considerations: Adopt a more complex layer stacking strategy and include nonlinear activation functions like tanh to deepen feature extraction. Use techniques like feature concatenation across layers to enrich the modelâs learning capacity.

Model Configuration and Setup: Implement advanced configurations with learning rate decay, sophisticated dropout schemes, and batch normalization as used in IGMC. These features will optimize the training process and enhance model performance.
4. Training and Testing the GNN Model

Validation and Model Tuning: Regularly use the validation dataset to refine the modelâs parameters. This helps in making sure the model does not overfit to the training data and maintains good performance on unseen data. Adjustments based on validation outcomes are crucial for improving the modelâs ability to generalize.

Handling the Test Set: For the test set, simulate realworld conditions by âmaskingâ or âdroppingâ some usermovie interactions that the model has not previously seen. This setup challenges the model to make predictions based on partial information, mirroring how it would function in a live environment.

Performance Evaluation: Evaluate the modelâs effectiveness using metrics such as Precision, Recall, and F1score. These metrics will help gauge how accurately the model can predict movie preferences for users based on incomplete interaction data. Assess how well the model can fill in the missing interactions in the test set, which is critical for a recommender system.

Continuous Monitoring: Monitor the loss curves for both training and validation phases to ensure there are no sudden shifts or divergences. Consider using tools like Weights & Biases (wandb) for this purpose. Itâs a free, cloudhosted service that provides realtime visualization of model metrics, making it easier to track performance and identify issues like overfitting early. Adopting such tools early can streamline your workflow and is a good practice in the machine learning community.

Hyperparameter Optimization: Adjust key settings such as the learning rate, dropout rate, and the number of GNN layers during the training process. Experiment with these parameters to optimize the balance between training loss minimization and validation performance maximization.
Code Snippets
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Hereâs a detailed breakdown of the code snippets for each step to guide you through setting up and implementing the GNN model for the Movie Recommender System:
1. Setup and Data Preparation
Make sure all necessary packages are installed and load the dataset:
# Ensure necessary libraries are installed, add more libraries as needed
!pip install torch==1.10.0 torchvision==0.11.1 torchaudio==0.10.0
!pip install torchgeometric==2.0.1
!pip install pandas networkx
import pandas as pd
# Load MovieLens data.
try:
movies = pd.read_csv('movies.csv')
ratings = pd.read_csv('ratings.csv')
print("Data loaded successfully!")
except FileNotFoundError:
print("Data files not found. Please check the files' path and try again.")
Steps for IGMC:
# Clone the IGMC repository
!git clone b latest https://github.com/muhanzhang/IGMC.git
# Move necessary files 'util_functions.py', 'data_utils.py', 'preprocessing.py' from the cloned repository to your current directory
2. Building the Graph Structure
Create a graph from the loaded data and prepare it for use with PyTorch Geometric:
import networkx as nx
from torch_geometric.utils import from_networkx
# Create a graph from the pandas DataFrame
# Nodes represent users and movies, edges represent user ratings for movies
G = nx.from_pandas_edgelist(ratings, 'user_id', 'movie_id', ['rating'])
# Convert the NetworkX graph to a PyTorch Geometric graph
G_torch = from_networkx(G)
Steps for IGMC:
import torch
from data_utils import load_official_trainvaltest_split
# Load and preprocess the MovieLens dataset using IGMC's utilities
(u_features, v_features, adj_train, train_labels, train_u_indices, train_v_indices,
val_labels, val_u_indices, val_v_indices, test_labels, test_u_indices, test_v_indices,
class_values) = load_official_trainvaltest_split('ml_100k', testing=True)
# Print to confirm the data is ready for further steps
print("Data loaded and preprocessed successfully using IGMC's tools.")
3. Implementing GNN Models
Define a basic GNN model architecture using PyTorch Geometric, ensuring to define the number of features and classes:
import torch
from torch_geometric.nn import GCNConv
class RecommenderGNN(torch.nn.Module):
def __init__(self, num_features, num_classes):
super(RecommenderGNN, self).__init__()
# Initialize two GCN layers
self.conv1 = GCNConv(num_features, 16) # First GCN layer to transform features
self.conv2 = GCNConv(16, num_classes) # Second GCN layer to prepare for output
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = torch.relu(self.conv1(x, edge_index)) # Apply ReLU activation function for nonlinearity
x = self.conv2(x, edge_index) # No activation before final layer
return x # Return the output directly for a regression or use sigmoid for binary classification
# Example instantiation of the model
# Ensure that 'your_num_features' and 'your_num_classes' are defined
# For instance, 'your_num_features' could be the number of movie genres, and 'your_num_classes' could be 1 for a rating prediction
model = RecommenderGNN(num_features=your_num_features, num_classes=your_num_classes)
import torch
from torch_geometric.nn import GATConv
class RecommenderGAT(torch.nn.Module):
def __init__(self, num_features, num_classes):
super(RecommenderGAT, self).__init__()
# Initialize two GAT layers
self.conv1 = GATConv(num_features, 16, heads=8, dropout=0.6) # First GAT layer with 8 attention heads
self.conv2 = GATConv(16 * 8, num_classes, heads=1, concat=False, dropout=0.6) # Second GAT layer
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index)) # Apply ReLU after the first GAT layer
x = F.dropout(x, p=0.6, training=self.training) # Dropout for regularization
x = self.conv2(x, edge_index) # No activation for regression
return x
# Example instantiation of the GAT model
model_gat = RecommenderGAT(num_features=your_num_features, num_classes=your_num_classes)
Steps for IGMC:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import RGCNConv, GraphConv
class RecommenderGNN(torch.nn.Module):
def __init__(self, num_features, num_classes, num_relations, num_bases):
super(RecommenderGNN, self).__init__()
self.conv1 = RGCNConv(num_features, 64, num_relations, num_bases=num_bases)
self.conv2 = GraphConv(64, 128)
self.conv3 = GraphConv(128, 256)
self.fc1 = nn.Linear(256, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, data):
x, edge_index, edge_type = data.x, data.edge_index, data.edge_type
x = self.conv1(x, edge_index, edge_type)
x = F.relu(x)
x = self.conv2(x, edge_index)
x = F.relu(x)
x = self.conv3(x, edge_index)
x = F.relu(x)
x = torch.mean(x, dim=0) # Global pooling
x = F.dropout(x, p=0.5, training=self.training)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
return x
# Example instantiation of the model
# Ensure that 'your_num_features', 'your_num_classes', 'your_num_relations', and 'your_num_bases' are defined
# For instance, 'your_num_relations' could be the number of edge types in your graph
# and 'your_num_bases' could be a hyperparameter for the RGCN layer
model = RecommenderGNN(num_features=your_num_features, num_classes=your_num_classes,
num_relations=your_num_relations, num_bases=your_num_bases)
4. Training and Testing the GNN Model
Setup the training loop and define the optimizer and loss function:
from torch.optim import Adam
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR
# Setup the training loop
optimizer = Adam(model.parameters(), lr=0.01)
criterion = torch.nn.MSELoss() # Using Mean Squared Error for regression
scheduler = StepLR(optimizer, step_size=50, gamma=0.1) # Reduces the learning rate by a factor of 0.1 every 50 epochs
# Example of how validation and test data might be structured, with some data intentionally masked to simulate missing interactions
# val_data, test_data = ...
for epoch in range(200):
model.train()
optimizer.zero_grad()
# Simulate missing interactions by masking some data entries
# This is critical for ensuring the model learns to predict unseen movie preferences
out = model(data) # Ensure 'data' is properly loaded and prepared
loss = criterion(out, data.y) # Assuming data.y are the actual ratings
loss.backward()
optimizer.step()
scheduler.step() # Adjust the learning rate
# Validation logic
if epoch % 10 == 0: # Check validation performance every 10 epochs
model.eval()
with torch.no_grad():
val_out = model(val_data)
val_loss = criterion(val_out, val_data.y)
print(f'Epoch {epoch}, Validation Loss: {val_loss.item()}')
# Early stopping logic to halt training if no improvement in validation loss
if should_stop_early(val_loss):
print("Stopping early due to lack of improvement in validation loss.")
break
print(f'Epoch {epoch}, Training Loss: {loss.item()}')
def should_stop_early(current_val_loss):
# Placeholder function for early stopping logic
# Implement logic to keep track of the best loss and stop if there's no improvement
pass
# Testing logic to evaluate the model's ability to predict new or missing interactions
# This might involve measuring the accuracy of predictions against truly unseen data
# test_out = model(test_data)
# test_loss = criterion(test_out, test_data.y)
# print(f'Test Loss: {test_loss.item()}')
Steps for IGMC:
from IGMC.models import IGMC_R_GCN
# Define the IGMC model
igmc_model = IGMC_R_GCN(u_features, v_features, adj_train, num_users, num_items, hidden_dim=64, num_relations=1, num_bases=4)
# Setup the training loop with IGMCspecific configurations
optimizer = Adam(igmc_model.parameters(), lr=0.01, weight_decay=1e4)
criterion = torch.nn.MSELoss()
scheduler = StepLR(optimizer, step_size=50, gamma=0.1)
for epoch in range(200):
igmc_model.train()
optimizer.zero_grad()
out = igmc_model(u_features, v_features, adj_train)
loss = criterion(out[train_u_indices, train_v_indices], train_labels)
loss.backward()
optimizer.step()
scheduler.step()
# Validation logic for IGMC model
if epoch % 10 == 0:
igmc_model.eval()
with torch.no_grad():
val_out = igmc_model(u_features, v_features, adj_train)
val_loss = criterion(val_out[val_u_indices, val_v_indices], val_labels)
print(f'Epoch {epoch}, Validation Loss: {val_loss.item()}')
if should_stop_early(val_loss):
print("Stopping early due to lack of improvement in validation loss.")
break
print(f'Epoch {epoch}, Training Loss: {loss.item()}')
import torch
def precision_at_k(predicted, truth, k):
"""
Computes the precision@k for the specified values of k
"""
batch_size = predicted.size(0)
_, ind = torch.topk(predicted, k, dim=1)
truth_resholded = truth.view(1, 1).expand_as(ind)
correct = ind.eq(truth_throughed)
correct_k = correct[:, :k].sum(dim=1).float()
return correct_k.sum() / batch_size
def recall_at_k(predicted, truth, k):
"""
Computes the recall@k for the specified values of k
"""
batch_size = predicted.size(0)
_, ind = torch.topk(predicted, k, dim=1)
truth_reshaped = truth.view(1, 1).expand_as(ind)
correct = ind.eq(truth_through)
total = truth.sum(dim=1)
hit_at_k = correct[:, :k].sum(dim=1)
return hit_at_k.sum().float() / total.sum()
def f1_at_k(predicted, truth, k):
"""
Computes the F1@k for the specified values of k
"""
precision = precision_at_k(predicted, truth, k)
recall = recall_at_k(predicted, truth, k)
return 2 * (precision * recall) / (precision + recall)
# Example usage
test_out = model(test_data)
precision_k = precision_at_k(test_out, test_data.y, k=10)
recall_k = recall_at_k(test_out, test_data.y, k=10)
f1_k = f1_at_k(test_out, test_data.y, k=10)
print(f'Precision@10: {precision_k}, Recall@10: {recall_k}, F1@10: {f1_k}')
Evaluation Process
For a comprehensive understanding of the evaluation process and STEMAway tacks, please take a moment to review the general details provided here. Familiarizing yourself with this information will ensure a smoother experience throughout the assessment.
For the first part of the evaluation (MCQ), please click on the evaluation button located at the end of the post. Applicants achieving a passing score of 8 out of 10 will be invited to the second round of evaluation.
Advancing to the Second Round:
If you possess the required expertise for an advanced conversation with the AI Evaluator, you may opt to bypass the virtual internships and directly pursue skill certifications.
Evaluation for our VirtualInternships Admissions

Start with a Brief Project Overview: Begin by summarizing the project objectives and the key technologies you used (GNNs, MovieLens dataset, PyTorch Geometric). This sets the context for the discussion.

Discuss Model Selection: Explain why you chose to implement GCN or GAT or both. Discuss your hypothesis on why one model might perform better than the other (e.g., âI thought GATâs attention mechanism would help capture the varying importance of different movie features for each userâ). Then, discuss the actual results and whether they aligned with your expectations.

Challenges and ProblemSolving: Present a specific challenge you faced, like handling coldstart users or dealing with sparse data. Explain your solution and how it impacted the modelâs performance. This shows critical thinking and problemsolving skills.

Insights from Experiments: Share an interesting finding from your experiments. For example, âI found that when I increased the number of attention heads in GAT from 4 to 8, my precision@10 improved by 15%. This suggests that capturing diverse aspects of usermovie interactions is crucial.â

Realworld Application: Discuss how you would deploy this model in production. Talk about concerns like latency (how quickly can you generate recommendations?), scalability (how does the model perform with a million users?), and coldstart problems (how do you recommend movies to new users?).

Learning and Growth: End by reflecting on your learning journey such as âWorking on this project, I gained a deep appreciation for how graph structures can capture complex interactions. I also realized the importance of data preprocessing  our initial results improved significantly after we properly handled missing ratings.â

Ask Questions: Show curiosity by asking the AI mentor questions. For example, âIâm curious, in largescale recommender systems like Netflix or Spotify, how do they balance the computational complexity of GNNs with the need for realtime recommendations?â
Evaluations for Skill Certifications for our Talent Discovery Platform

Model Accuracy and Insights:
 Accuracy Metrics: Be prepared to discuss the accuracy of your GNN model using metrics like MSE or RMSE. Have examples ready where the model performed well or fell short.
 Specific Cases: Think about particular instances where the modelâs predictions were notably accurate or inaccurate. Consider what these cases might indicate about the modelâs strengths and weaknesses.

Trends and Performance Analysis:
 Loss Trends: Describe the trends observed in your training and validation loss curves. Discuss any concerns about overfitting or underfitting and what steps you took to address them.
 Impactful Epochs: Identify key epochs where changes in model performance were observed and what you learned from these points.

Scalability Considerations:
 Dataset Influence: Share how alterations in the dataset size affected the modelâs performance and discuss any scalability challenges you encountered.
 Resource Management: Talk about the computational resources required as you scaled the model and how practical these requirements are for broader applications.

Comparative Analysis:
 Against Baselines: Bring up how your model compares to simpler baseline models like collaborative filtering or matrix factorization. Highlight the advantages of using GNNs based on your findings.
 Feature Utilization: Discuss how effectively your model uses the available data features compared to the baselines, and what that implies about the modelâs complexity and capability.

Exploration and Experimentation:
 Advanced Techniques: If you experimented with advanced techniques like graphlevel dropout or different GNN layers, describe these experiments and their outcomes.
 Innovative Approaches: Reflect on any novel approaches you tried, how they affected the results, and what you learned from these experiments.
Resources and Learning Materials
Online Resources
 PyTorch Geometric Documentation: Dive into the official PyTorch Geometric documentation for detailed guides on how to implement various GNN architectures, along with a plethora of practical examples and API descriptions.
 Google AI Blog: Recommender Systems with Graph Neural Networks: https://ai.googleblog.com/2020/08/recommendersystemswithgraphneural.html
 TensorFlow Graph Neural Networks: https://www.tensorflow.org/neural_networks/graphs/guides/graph_neural_networks
 Awesome Graph Neural Networks: GitHub  thunlp/GNNPapers: Mustread papers on graph neural networks (GNN)
Research Papers:
 Zhang, M., & Chen, Y. (2020). Inductive Matrix Completion Based on Graph Neural Networks. International Conference on Learning Representations (ICLR). [Paper Link] This is the foundational paper that introduced the IGMC framework, which is a key component of the advanced implementation in this project.
 Monti, F., Bronstein, M., & Bresson, X. (2017). Geometric Matrix Completion with Recurrent MultiGraph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS). [Paper Link] This paper explores the use of graph neural networks for matrix completion tasks, which is relevant to the recommender system problem.
 Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018). Graph Convolutional Neural Networks for WebScale Recommender Systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [Paper Link] This paper discusses the application of graph convolutional neural networks (GCNs) for largescale recommender systems, providing insights into the practical considerations and scalability of such models.