Retrieval models for audience selection in display advertising

Sarah K. Tyler, Sandeep Pandey, Evgeniy Gabrilovich, Vanja Josifovski
<span title="">2011</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM &#39;11</a> </i> &nbsp;
Web applications often rely on user profiles of observed user actions, such as queries issued, page views, etc. In audience selection for display advertising, the audience that is likely to be responsive to a given ad campaign is identified via such profiles. We formalize the audience selection problem as a ranked retrieval task over an index of known users. We focus on the common case of audience selection where a small seed set of users who have previously responded positively to the campaign
more &raquo; ... is used to identify a broader target audience. The actions of the users in the seed set are aggregated to construct a query, the query is then executed against an index of other user profiles to retrieve the highest scoring profiles. validate our approach on a real-world dataset, demonstrating the trade-offs of different user and query models and find that our approach is particularly robust for small campaigns. The proposed user modeling framework is applicable to many other applications requiring user profiles such as content suggestion and personalization.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2063576.2063664">doi:10.1145/2063576.2063664</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/TylerPGJ11.html">dblp:conf/cikm/TylerPGJ11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oc3bwehluvdzxpjaf42awxrjee">fatcat:oc3bwehluvdzxpjaf42awxrjee</a> </span>
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