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When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.DISC.2017.2
URN: urn:nbn:de:0030-drops-79652
URL: http://drops.dagstuhl.de/opus/volltexte/2017/7965/
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Kermarrec, Anne-Marie

Recommenders: from the Lab to the Wild (Keynote Talk)

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LIPIcs-DISC-2017-2.pdf (0.2 MB)


Abstract

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.

BibTeX - Entry

@InProceedings{kermarrec:LIPIcs:2017:7965,
  author =	{Anne-Marie Kermarrec},
  title =	{{Recommenders: from the Lab to the Wild (Keynote Talk)}},
  booktitle =	{31st International Symposium on Distributed Computing (DISC 2017)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-053-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{91},
  editor =	{Andr{\'e}a W. Richa},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7965},
  URN =		{urn:nbn:de:0030-drops-79652},
  doi =		{10.4230/LIPIcs.DISC.2017.2},
  annote =	{Keywords: Recommenders, Collaborative filtering, Distributed systems}
}

Keywords: Recommenders, Collaborative filtering, Distributed systems
Seminar: 31st International Symposium on Distributed Computing (DISC 2017)
Issue Date: 2017
Date of publication: 05.10.2017


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