Deep Recommendation using Graphs

This tutorial is part of chapters six and eight from my recent book "Deep Recommender Systems with Python", which can be found in https://www.panagiotissymeonidis.com/enbook/index.html. In particular, this tutorial is a comprehensive exploration of graph-based methods to enhance recommender systems. The presentation is structured around key topics, starting with the basics of graphs and recommender systems, and progresses to advanced techniques involving Graph Neural Networks (GNNs) and their applications. It offers a rich blend of theory and practice. It is important for the Recommender Systems community because it analyzes graph-based algorithms to address the similarity search and recommendation problem. Both problems affect our everyday experience, while searching for knowledge on a topic with challenging issues such as data scalability, noise, and sparsity. We can deal with all these challenges by applying advanced graph-based methods. In this tutorial, we provide a detailed step-by-step analysis with an integrated toy example in three jupyter notebook sessions, which helps the audience clearly understand the main differences of basic graph-based methods.