Graph-based Representation Learning for Web-scale Recommender Systems

Ahmed El-Kishky, Michael Bronstein, Ying Xiao, Aria Haghighi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages4784-4785
Number of pages2
ISBN (Electronic)9781450393850
DOIs
StatePublished - 14 Aug 2022
Externally publishedYes
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: 14 Aug 202218 Aug 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States
CityWashington
Period14/08/2218/08/22

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