A Comparative Study of Click Models for Web Search

  • Artem Grotov
  • Aleksandr Chuklin
  • Ilya MarkovEmail author
  • Luka Stout
  • Finde Xumara
  • Maarten de Rijke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)


Click models have become an essential tool for understanding user behavior on a search engine result page, running simulated experiments and predicting relevance. Dozens of click models have been proposed, all aiming to tackle problems stemming from the complexity of user behavior or of contemporary result pages. Many models have been evaluated using proprietary data, hence the results are hard to reproduce. The choice of baseline models is not always motivated and the fairness of such comparisons may be questioned. In this study, we perform a detailed analysis of all major click models for web search ranging from very simplistic to very complex. We employ a publicly available dataset, open-source software and a range of evaluation techniques, which makes our results both representative and reproducible. We also analyze the query space to show what type of queries each model can handle best.


Cascade Model Dynamic Bayesian Network Search Session Query Frequency Relevance Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Artem Grotov
    • 1
  • Aleksandr Chuklin
    • 1
  • Ilya Markov
    • 1
    Email author
  • Luka Stout
    • 1
  • Finde Xumara
    • 1
  • Maarten de Rijke
    • 1
  1. 1.University of AmsterdamAmsterdamThe Netherlands

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