Keynote Talk: Recommenders: from the lab to the wild
Mediego, Inria (France), EPFL (Switzerland)
Anne-Marie Kermarrec is a senior researcher at Inria, France where she led a research group on large-scale distributed systems from 2006 to 2015. She is currently the CEO of Mediego startup that she founded in April 2015. Mediego provides online predictive marketing services that directly leverage her recent research. She is also affiliated with EPFL, Swizerland. Before that, after her PhD thesis at University of Rennes in 1996, she has been with Vrije Universiteit. NL and Microsoft Research Cambridge, UK.
Anne-Marie received an ERC grant in 2008 and an ERC proof of Concept in 2013. She has been member of the ACM Software System Award Committee from 2009 to 2014 and chaired that committed in 2012 and 2014. She was the vice-chair of ACM Sigops Eurosys from 2013 to 2017. She received the Montpetit Award from the French Academy of Science in 2011, the innovation award from the French Academy of Scienc in 2017 and is a member of the European Academy since 2013. She was named a 2016 ACM Fellow for contributions to large-scale distributed computing.
Her research interests are in large-scale distributed systems and recommenders. Her research work was pioneering in peer-to-peer (P2P) and gossip-based algorithms, as well as large-scale personalization infrastructures. She has been also every active in the area of privacy-aware recommenders. 18 students got their PhD under her supervision. She published more than 200 academic papers and received several best papers awards including the Test of time award at ACM/IEEE/ICIP Middleware conference in 2014 for her work on gossip-based peer sampling.
Recommenders are ubiquitous on the Internet today: they tell you which book to read, which movie you should watch, predict your next holiday destination, give you advices on restaurants and hotels, they are even responsible for the posts that you see on your favorite social media and potentially greatly influence your friendship on social networks.
While many approaches exist, collaborative filtering is one of the most popular approaches to build online recommenders that provide users with content that matches their interest. Interestingly, the very notion of users can be general and span actual humans or software applications. Recommenders come with many challenges beyond the quality of the recommendations. One of the most prominent ones is their ability to scale to a large number of users and a growing volume of data to provide real-time recommendations introducing many system challenges. Another challenge is related to privacy awareness: while recommenders rely on the very fact that users give away information about themselves, this potentially raises some privacy concerns.
In this talk, I will focus on the challenges associated to building efficient, scalable and privacy-aware recommenders.
March 5th, 2021 @23:59 HST
Abstract submission deadline
March 12th, 2021 @23:59 HST
Paper submission deadline
April 26th, 2021
May 3rd, 2021 @23:59 HST
Camera ready copy due
May 10th, 2021 @23:59 HST
May 19th-21st, 2021
Online Conference with pre-recorded talks
Revised selected papers will be published as a post-proceedings in Springer's LNCS "Lecture Notes in Computer Science"
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