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UID:83@lincs.fr
DTSTART;TZID=Europe/Paris:20150715T140000
DTEND;TZID=Europe/Paris:20150715T150000
DTSTAMP:20170313T170943Z
URL:https://www.lincs.fr/events/learning-to-rank-regret-lower-bounds-and-e
 fficient-algorithms/
SUMMARY:Learning to Rank: Regret Lower Bounds and Efficient Algorithms
DESCRIPTION:Algorithms for learning to rank Web documents\, display ads\,
 or other types of items constitute a fundamental component of search
 engines and more generally of online services. In such systems\, when a
 user makes a request or visits a web page\, an ordered list of items (e.g.
 documents or ads) is displayed\; the user scans this list in order\, and
 clicks on the first relevant item if any. When the user clicks on an item\,
 the reward collected by the system typically decreases with the position of
 the item in the displayed list. The main challenge in the design of
 sequential list selection algorithms stems from the fact that the
 probabilities with which the user clicks on the various items are unknown
 and need to be learned. We formulate the design of such algorithms as a
 stochastic bandit optimization problem. This problem differs from the
 classical bandit framework: (1) the type of feedback received by the system
 depends on the actual relevance of the various items in the displayed list
 (if the user clicks on the last item\, we know that none of the previous
 items in the list are relevant)\; (2) there are inherent correlations
 between the average relevance of the items (e.g. the user may be interested
 in a specific topic only). We assume that items are categorized according
 to their topic and that users are clustered\, so that users of the same
 cluster are interested in the same topic. We investigate several scenarios
 depending on the available side-information on the user before selecting
 the displayed list: (a) we first treat the case where the topic the user is
 interested in is known when she places a request\; (b) we then study the
 case where the user cluster is known but the mapping between user clusters
 and topics is unknown. For both scenarios\, we derive regret lower bounds
 and devise algorithms that approach these fundamental limits.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Meeting Room 40\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.8283983;2.3568972000000485
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20150329T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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