Friday April 9 2021
The program will consist of a series of plenary talks and a Q&A session for submitted work. Prior to the real-time webinar, authors of contributed work will submit pre-recorded presentations of their talks (see call for participation below). During the Q&A session authors will have a chance to answer questions regarding their work.
If you are attending PeRSonAl at MLSys 2021 please, optionally, add yourself to this Google spreadsheet. The sheet helps us understand the background of participants and organize future workshops.
|9:15am – 9:30am||Welcome to the 3rd PeRSonAl Workshop||Carole-Jean Wu/FAIR; Udit Gupta/FAIR-Harvard|
|9:30am – 10:00am||Explainable ML for Recommender Systems: Challenges and Opportunities||Hima Lakkaraju/Harvard University|
|10:00am – 10:30am||A Memory-centric Approach in Designing System Architectures for Personalized Recommendations||Minsoo Rhu/KAIST|
|10:30am – 10:45am||MERCI: Efficient Embedding Reduction on Commodity Hardware via Sub-Query Memoization. ||Yejin Lee, Seong Hoon Seo, Hyunji Choi, Hyoung Uk Sul, Soosung Kim, Jae W. Lee, Tae Jun Ham (Seoul National University)|
|10:45am – 11:00am||Erasure Coding Based Fault Tolerance for Recommendation Model Training||Kaige Liu (Facebook), Jack Kosaian, Rashmi Vinayak (CMU)|
|11:00am – 11:15am||Elliot: A Comprehensive and Rigorous Framework For Reproducible Recommender Systems Evaluation|
Vito Walter Anelli (Polytechnic University of Bari), Alejandro Bellogín (Universidad Autónoma de Madrid), Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo (Polytechnic University of Bari), Francesco Maria Donini (Università della Tuscia), Tommaso Di Noia (Polytechnic University of Bari)
|11:15am – 11:30am||Optimizing Deep Learning Recommender SystemsTraining on CPU Cluster Architectures||Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, and Alexander Heinecke (Intel)|
|11:30am – 11:45am||Main-Memory Acceleration for Bandwidth-Bound Deep Learning Inference||Benjamin Cho, Jeageun Jeung, Mattan Erez (UT Austin)|
|11:45am – 12:00pm||DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference||Udit Gupta (Harvard University/FAIR), Samuel Hsia (Harvard University), Vikram Saraph, Xiaodong Wang, Brandon Reagen (Facebook), Gu-Yeon Wei (Harvard University), Hsien-Hsin S. Lee (FAIR), David Brooks (Harvard University), Carole-Jean Wu (FAIR)|
|12:00pm – 1:00pm||Coffee/Lunch Break|
|1:00pm – 2:00pm||Keynote: From Recommender Systems to Natural Language Processing and Back Again||Julian McAuley/UCSD|
|2:00 — 2:30 pm||Revisiting Recommender Systems on the GPU||Even Oldridge/NVIDIA|
|2:30pm – 3:00pm||Coffee/Lunch Break|
|3:00pm – 3:30pm||Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale||Summer Deng/Facebook|
|3:30pm – 4:00pm||Pushing the Limits of Recommender Training Speed: An MLPerf Experience||Tayo Oguntebi/Google|
|4:00pm – 4:15pm||Cross-Stack Workload Characterization of Deep Recommendation Systems||Samuel Hsia, Udit Gupta, Mark Wilkening (Harvard University), Carole-Jean Wu (FAIR), Gu-Yeon Wei, David Brooks (Harvard University)|
|4:15pm – 4:30pm||Accelerated Learning by Exploiting Popular Choices||Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan (Microsoft), Prashant Nair (University of British Columbia)|
|4:30pm – 4:45pm||Towards Disaggregated Memory Recommenders||Talha Imran (Penn State), Nadav Amit, Irina Calciu (VMWare Research)|
|4:45pm – 5:00pm||Scalability, Latency, Flexibility: The Case for Similarity Search as a Service||Amir Sadoughi, Edo Liberty, Lior Ehrenfeld, Ron Begleiter, Fei Yu, Mark Chew, Jack Pertschuk, Roei Mutay, Greg Kogan, Beni Ran (Pinecone)|
|5:00pm – 5:15pm||Capacity-Driven Scale-Out Neural Recommendation: Enabling the Growing Scale of Recommendation||Mike Lui (Drexel University, Facebook), Yavuz Yetim, Oz Ozkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu (Facebook), Mark Hempstead (Tufts Unviersity)|
|5:15pm – 5:30pm||Training with Multi-Layer Embeddings for Model Reduction||Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho, Leo Orshansky (UT Austin), Ashish Kumar Singh (E2OPEN), Mattan Erez, Michael Orshansky (UT Austin)|
|5:30pm – 5:45pm||Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction||Qingquan Song (Texas A&M University), Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian (Facebook), Xia Hu (Texas A&M University)|
|5:45pm – 6:00pm||Closing session||Carole-Jean Wu/FAIR; Udit Gupta/FAIR-Harvard|
Details for the contributed talks, including the abstracts and speaker bio’s can be found here.
Call for Talk Participation
Personalized recommendation is the process of ranking and recommending content based on users’ personal preferences. Recommendation algorithms are central to providing personalized search results, marketing strategies, e-commerce product suggestions, and entertainment content. Given the pervasive use of personalized recommendations across many Internet services, state-of-the-art recommendation algorithms are using increasingly more sophisticated machine learning approaches. These advances have led to personalized recommendation algorithms consuming a large fraction, and in many cases the majority, of AI cycles and datacenter capacity. Thus, the unique demands of recommendation algorithms must be met with innovative solutions across the computing stack.
The PeRSonAl workshop invites submissions across all sub-areas in algorithms, datasets, and systems and hardware related to personalized recommendation. Topics of interest include but are not limited to:
- Emerging algorithms for personalized recommendation
- Datasets to train and test recommendation algorithms
- Specialized systems and hardware
- Novel applications of recommendation algorithms
- Case studies and prototypes of training and deploying recommendation systems
As the workshop will be hosted virtually it will comprise pre-recorded, 30-minute presentations based on authors’ submissions. Submissions can be up to 2 pages excluding reference (following the same formatting guidelines as the conference). Submissions should be sent to email@example.com and firstname.lastname@example.org.
- Paper submission deadline: March 26, 2021
- Paper notification: March 29, 2021
- Pre-recorded presentation deadline: April 5, 2021
- PeRSonAl workshop: April 9, 2021