Graph-based Representation Learning for Web-scale Recommender Systems

Abstract

Recommender systems are fundamental building blocks of modern consumer web applications that seek to predict user preferences to better serve relevant items. As such, high-quality user and item representations as inputs to recommender systems are crucial for personalized recommendation. To construct these user and item representations, self-supervised graph embedding has emerged as a principled approach to embed relational data such as user social graphs, user membership graphs, user-item engagements, and other heterogeneous graphs. In this tutorial we discuss different families of approaches to self-supervised graph embedding. Within each family, we outline a variety of techniques, their merits and disadvantages, and expound on latest works. Finally, we demonstrate how to effectively utilize the resultant large embedding tables to improve candidate retrieval and ranking in modern industry-scale deep-learning recommender systems.

Outline

  1. Introduction and Motivation [slides]
  2. Homogenous Graph Embeddings [slides]
  3. Heterogeneous Graph Embeddings [slides]

Code & Data

  • Scalable Heterogenous Embeddings: [TwHIN]
  • knn-Embed: [kNN-Embed]
  • Publications

    PEOPLE