Online Recommender Systems: Is the Juice Worth the
Squeeze?
Keynote

Eugene Yan1
1
    Amazon, Seattle, WA, USA


                                         Abstract
                                         Online recommender systems are increasingly prevalent given their ability to adapt to the customer’s
                                         needs in real time. Nonetheless, they come with additional costs (computation, operational) and complex-
                                         ity (infrastructure). In this keynote, we explore when it makes sense to use an online recommender and
                                         when a batch recommender is good enough. Then, to better understand the differentiating strengths of
                                         online recommenders, we share three systems at Amazon Books that play to these strengths, high-level
                                         results, and lessons from making them work in the field.




Speaker biography
Eugene Yan is a Senior Applied Scientist at Amazon where he builds machine learning and
recommender systems. His interests lie in applying machine learning to industrial systems
that serve customers at scale. His current work at Amazon focuses on session-based candidate
retrieval, bandit-based ranking, and recommendations in search. Previously, he led the data
science teams at Lazada (acquired by Alibaba) and uCare.ai (Series A healthtech).




ORSUM@ACM RecSys 2022: 5th Workshop on Online Recommender Systems and User Modeling, jointly with the 16th
ACM Conference on Recommender Systems, September 23rd, 2022, Seattle, WA, USA
                                       © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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    Proceedings
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                  ISSN 1613-0073
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