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UID:625@lincs.fr
DTSTART;TZID=Europe/Paris:20210428T140000
DTEND;TZID=Europe/Paris:20210428T150000
DTSTAMP:20210429T073822Z
URL:https://www.lincs.fr/events/seminar-talk-by-theodoros-giannakas/
SUMMARY:SOBA: Session optimal MDP-based network friendly recommendations
DESCRIPTION:Caching content over CDNs or at the network edge has been
 solidified as a means to improve network cost and offer better streaming
 experience to users. Furthermore\, nudging the users towards low-cost
 content has recently gained momentum as a strategy to boost network
 performance. We focus on the problem of optimal policy design for Network
 Friendly Recommendations (NFR). We depart from recent modeling attempts\,
 and propose a Markov Decision Process (MDP) formulation. MDPs offer a
 unified framework that can model a user with random session length. As it
 turns out\, many state-of-the-art approaches can be cast as subcases of our
 MDP formulation. Moreover\, the approach offers flexibility to model users
 who are reactive to the quality of the received recommendations. In terms
 of performance\, for users consuming an arbitrary number of contents in
 sequence\, we show theoretically and using extensive validation over real
 traces that the MDP approach outperforms myopic algorithms both in session
 cost as well as in offered recommendation quality. Finally\, even compared
 to optimal state-of-art algorithms targeting specific subcases\, our MDP
 framework is significantly more efficient\, speeding the execution time by
 a factor of 10\, and enjoying better scaling with the content catalog and
 recommendation batch sizes.
CATEGORIES:Seminars,Youtube
LOCATION:Zoom + LINCS\, 23 avenue d'Italie\, Paris\, 75013\, France
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=Zoom + LINCS:geo:0,0
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TZID:Europe/Paris
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DTSTART:20210328T030000
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