Recommender systems are ubiquitous across many domains such as entertainment, e-commerce, news, social media, fitness, e-commerce and education, and as such, the need to think about the problems at scale is ever growing. As the Scaling Law in Recommender Systems becomes more apparent, the demand for billion-parameter or even trillion-parameter models in Recommender Systems is increasing to unprecedented levels. When implemented within industry settings, these systems encounter many real-world constraints and complexities that require tailored solutions. Designing and maintaining recommender systems that operate at a massive scale come with their own set of distinct algorithmic, systematic, and ethical challenges.
LargeRecSys workshop aims to bridge the gap between state-of-the-art RecSys research and the practical demands of operating large recommender systems at scale. We plan to invite researchers and practitioners from both industry and academia to engage in discussions and challenges inherent in large-scale recommender systems.
We invite submissions in two formats: extended abstracts (1-8 pages), or slides (15-20 slides). By accepting slides, we hope to lower the writing burden for industry participants. However, since slides submissions sometimes are short on details, we might request clarification or additional editing as condition for acceptance. We encourage contributions in new theoretical research, practical solutions to particular aspects of scaling a recommender, best practices in scaling evaluation systems, and creative new applications of big data to large scale recommendation systems.
Our topics of interests include, but are not limited to:
Lexi Baugher is a Distinguished Software Engineer at YouTube. She got her Bachelor of Science from Caltech in Engineering and Applied Science. She has worked at Google for 23 years. She is currently the Area Tech Lead of YouTube Discovery, overseeing development of YouTube's recommendation systems. She lives in San Francisco with her husband, kid, and cat; and enjoys baseball, running and baking.
Justin Basilico is a research/engineering director at Netflix. He leads the recommendations algorithms team that does research and development of the machine learning algorithms used to power the core Netflix experience and personalize it for our members worldwide. Prior to Netflix, he worked in the Cognitive Systems group at Sandia National Laboratories. He has an MS in Computer Science from Brown University and a BA in Computer Science from Pomona College.
Paper Submission Deadline: August 30, 2024
Reviewers Deadline: September 11, 2024
Author Notification: September 13, 2024
Camera-Ready Deadline: September 20, 2024