What is recommendation? Personalized recommendation is the process of ranking a large collection of items based on users’ preferences. Accurate and efficient ranking of items enables providing personalized search results, marketing strategies, e-commerce production suggestions, and entertainment content.
Where is neural recommendation used? DNN-based personalized recommendation models are at the heart of many Internet services including search, social media, and online entertainment. These models also consume a significant fraction of computation cycles and infrastructure capacity devoted to AI in the datacenter and pose unique at-scale system design challenges for efficient training and inference.
Why do we need a tutorial on neural recommender systems? Despite the importance of DNN-based personalized recommendation models and their unique compute demands, relatively little research attention has been devoted to recommendation systems. To address this gap in the research community, this tutorial serves as a venue to discuss, share, and foster research for personalized recommendation systems.
- Providing historical context into the evolution of recommendation systems
- Discussing challenges of state-of-the-art recommendation systems
- Providing a hands-on tutorial on open-source benchmarks and datasets (training and inference)
- Brainstorming novel solutions for efficient personalized recommendation.
|11:00am – 11:15am||Welcome to Inaugural PeRSonAl Tutorial!||Udit Gupta and Carole-Jean Wu (Harvard and Facebook)|
|11:15am – 11:45am||Recommendation Science in the Criteo AI Lab: From Practical Applications to Theoretical Research and Back||David Rohde (Criteo AI Labs)|
|11:45am – 12:15pm||Design Implications of Memory Systems and Near-Memory Processing for Personalized Recommendation||Xuan (Silvia) Zhang (Washington University/Facebook|
|12:15pm – 12:45pm||Training Deep Learning Recommendation Models||Maxim Naumov (Facebook)|
|12:45pm – 1:15pm||Building Production-Ready Recommendation Systems at Scale with Microsoft||Andreas Argyriou (Microsoft)|
|1:15pm – 1:45pm||At-scale Inference for Recommendation Systems||Udit Gupta (Harvard University/Facebook)|
A detailed program can be found at PeRSonAl at ASPLOS 2020
|Udit Gupta is a 4th year PhD student in CS at Harvard University and received his B.S. in ECE from Cornell University in 2016. His research interests focus on improving the performance and energy efficiency of emerging applications in computer systems and architecture by co-designing solutions across the computing stack. His recent work explores the characterization and optimization of at-scale deployment of deep learning based personalized recommendation systems.|
|David Brooks received his B.S. from University of Southern California in EE in 1997 and his M.A. and Ph.D. in EE from Princeton in 2001. He spent a year at IBM T.J. Watson Research Center in 2001 before joining Harvard in 2002. His research focuses on the interaction between the architecture and software of computer systems and underlying hardware implementation challenges, including power, reliability, and variability issues across embedded and high-performance computing systems.|
|Gu-Yeon Wei received his B.S.E.E., M.S., and Ph.D. in Electrical Engineering from Stanford University in 1994, 1997, and 2001, respectively. In 2000, he joined Accelerant Networks (now a part of Synopsys) in Beaverton, Oregon as a Senior Design Engineer. In 2002, he joined Harvard University. His research interests span a variety of topics such as integrated voltage regulators, flexible voltage stacking, power electronics, low-power computing architectures and circuits, auto-parallelizing compilers, and more.|
|Brandon Reagen is an Assistant Professor at NYU in the Department of Electrical and Computer Engineering with affiliation appointments in the Computer Science. A computer architect by training, Brandon has a research focus on designing specialized hardware accelerators for applications including deep learning and privacy preserving computation. He has made several contributions to ease the use accelerators as general architectural constructs including benchmarking, simulation infrastructure, and System on a Chip (SoC) design. He earned a PhD in computer science from Harvard in 2018 and received his undergraduate degrees in computer systems engineering and applied mathematics from the University of Massachusetts, Amherst, in 2012.|
|Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research interests are in computer architecture with particular focus on energy- and memory-efficient systems. More recently, her research has pivoted into designing systems for machine learning execution at-scale, such as personalized recommender systems, and mobile deployment. Carole-Jean chairs MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference. Carole-Jean holds tenure from ASU and received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards.|
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