PeRSonAl (Personalized Recommendation Systems and Algorithms) is an interdisciplinary tutorial with the goal to encourage research in three important research pillars — systems, algorithms, and datasets — in AI for efficient and responsible personalized recommendation systems.

About

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.

Recommendation models consume the majority (over 80%) of AI inference cycles spent in Facebook’s datacenters.

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. 

Details…

Tutorial Goals

  1. Providing historical context into the evolution of recommendation systems
  2. Discussing challenges of state-of-the-art recommendation systems
  3. Providing a hands-on tutorial on open-source benchmarks and datasets (training and inference)
  4. Brainstorming novel solutions for efficient personalized recommendation.

Program

The second iteration of the PeRSonAl tutorial will be held (virtually) in conjunction with ISCA 2020. PeRSonAl will include a real-time webinar, held on May 29 from 12:00pm EDT to 2:30pm EDT, consisting of three invited plenary talks, a Q&A session for submitted work, and a panel of experts on recommendation systems.

Authors, prior to the real-time webinar, will submit pre-recorded presentations of their talks. During the Q&A session authors will have a chance to answer questions regarding their work.

Time (EDT)TopicPresenter
12:00 – 12:15pmIntroduction to PeRSonAlCarole-Jean Wu (FAIR)
12:15 – 12:45pmPlenary Talk 1: Time, Context and Causality in Recommender SystemsYves Raimond (Netflix)
12:45 – 1:15pmPlenary Talk 2: Ins and Outs of Using GPUs for Training Recommendation ModelsBilge Acun (FAIR)
1:15 – 1:45pmPlenary Talk 3: Training Massive Scale Deep Learning Ads Systems with GPUs and SSDsWeijie Zhao (Baidu Research)
1:45 – 2:15pmResearch papers Q&A

A detailed program and Call for Participation can be found at PeRSonAl at ISCA 2020

Tutorial Venue

  1. Time: May 29, 2020
  2. Location: The 2nd PeRSonAl tutorial will be held virtually in conjunction with ISCA 2020.

Organizers

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.
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.

Contact Us

Please contact us at: ugupta@g.harvard.edu