IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
How well does result relevance predict session satisfaction?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
PSkip: estimating relevance ranking quality from web search clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.