Adaptation of offline vertical selection predictions in the presence of user feedback

  • Authors:
  • Fernando Diaz;Jaime Arguello

  • Affiliations:
  • Yahoo! Labs Montreal, Montreal, PQ, Canada;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2009

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Abstract

Web search results often integrate content from specialized corpora known as verticals. Given a query, one important aspect of aggregated search is the selection of relevant verticals from a set of candidate verticals. One drawback to previous approaches to vertical selection is that methods have not explicitly modeled user feedback. However, production search systems often record a variety of feedback information. In this paper, we present algorithms for vertical selection which adapt to user feedback. We evaluate algorithms using a novel simulator which models performance of a vertical selector situated in realistic query traffic.