To Swing or not to Swing: Learning when (not) to Advertise
Source:
ACM Conference on Information and Knowledge Management (CIKM) (2008)
Abstract:
Web textual advertising can be interpreted as a search problem
over the corpus of ads available for display in a particular
context. In contrast to conventional information retrieval
systems, which always return results if the corpus
contains any documents lexically related to the query, in
Web advertising it is acceptable, and occasionally even desirable,
not to show any results. When no ads are relevant
to the user’s interests, then showing irrelevant ads should be
avoided since they annoy the user and produce no economic
benefit. In this paper we pose a decision problem “whether
to swing”, that is, whether or not to show any of the ads
for the incoming request. We propose two methods for addressing
this problem, a simple thresholding approach and a
machine learning approach, which collectively analyzes the
set of candidate ads augmented with external knowledge.
Our experimental evaluation, based on over 28,000 editorial
judgments, shows that we are able to predict, with high
accuracy, when to “swing” for both content match and sponsored
search advertising.
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