Co-optimization of multiple relevance metrics in web search

  • Authors:
  • Dong Wang;Chenguang Zhu;Weizhu Chen;Gang Wang;Zheng Chen

  • Affiliations:
  • Tsinghua University and Microsoft Research Asia, Beijing, China;Tsinghua University and Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 19th international conference on World wide web
  • Year:
  • 2010

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Abstract

Several relevance metrics, such as NDCG, precision and pSkip, are proposed to measure search relevance, where different metrics try to characterize search relevance from different perspectives. Yet we empirically find that the direct optimization of one metric cannot always achieve the optimal ranking of another metric. In this paper, we propose two novel relevance optimization approaches, which take different metrics into a global consideration where the objective is to achieve an ideal tradeoff between different metrics. To achieve this objective, we propose to co-optimize multiple relevance metrics and show their effectiveness.