At a Glance

Name: Light Graph Convolutional Network for Recommender Systems
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: joseph_santarcangelo, wojciech_fulmyk, zikai_dou

Learn how to build a recommender system using Graph Convolutional Networks (GCN) with the LightGCN model. In this guided project, you’ll construct a user–item interaction graph, implement LightGCN in PyTorch, and evaluate it using Recall@K and NDCG@K. By the end, you’ll understand the theoretical foundations of LightGCN and apply it effectively to real recommendation tasks. You will also explore how message passing captures multi-hop collaborative signals, gaining a complete practical workflow for modern graph-based recommendation while learning to analyze embedding behavior in depth.

In this project, you’ll walk through each stage of building a modern graph-based recommender system, from preparing the interaction data to implementing message passing and training the LightGCN model. You’ll gain both conceptual understanding and hands-on experience with real datasets and a complete end-to-end recommendation pipeline..

Who Is It For

This project is designed for software developers, data scientists, or AI practitioners interested in Graph Neural Networks (GNNs) and their applications in recommender systems. It is ideal for learners who want to understand how Graph Convolutional Networks (GCNs) model relational data such as user–item interactions, explore how LightGCN streamlines traditional GCN architectures to better handle large-scale recommendation scenarios, and gain practical experience by implementing a real graph-based recommendation pipeline that connects theoretical concepts directly to hands-on practice.

What You’ll Learn

By the end of this project, you will be able to:

Understand the theoretical foundations of Graph Convolutional Networks (GCNs) and how LightGCN simplifies them.
Build and train a LightGCN recommender system in PyTorch using the MovieLens dataset.
Evaluate the model using Recall@K and NDCG@K metrics, and generate Top-K recommendations.

What You'll Need

Learners should have proficiency in Python and prior experience with interactive coding environments. A working knowledge of machine learning fundamentals, including embeddings, loss functions, and evaluation metrics, is recommended. All required libraries can be installed directly within the IBM Skills Network Labs environment, allowing you to set up and run the workflow without external configuration. The project works best on modern browsers such as Chrome, Edge, Firefox, or Safari.

Offered By: IBMSkillsNetwork

Light Graph Convolutional Network for Recommender Systems

At a Glance

Who Is It For

What You’ll Learn

What You'll Need