Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification

@article{Xu2017DirectlyOD,
  title={Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification},
  author={Jun Xu and Long Xia and Yanyan Lan and Jiafeng Guo and Xueqi Cheng},
  journal={ACM TIST},
  year={2017},
  volume={8},
  pages={41:1-41:26}
}
The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification. Recently, the relational learning to rank model, which designs a learnable ranking function following the criterion of maximal marginal relevance, has shown effectiveness in search result diversification [Zhu et al. 2014]. The goodness of a diverse ranking… CONTINUE READING
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