Incorporating user preferences into click models

  • Authors:

  • Qianli Xing;Yiqun Liu;Jian-Yun Nie;Min Zhang;Shaoping Ma;Kuo Zhang

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology, Beijing, China;Tsinghua National Laboratory for Information Science and Technology, Beijing, China;University of Montreal, Montreal, Canada;Tsinghua National Laboratory for Information Science and Technology, Beijing, China;Tsinghua National Laboratory for Information Science and Technology, Beijing, China;Tsinghua National Laboratory for Information Science and Technology, Beijing, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

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Abstract

Click models are developed to interpret clicks by making assumptions on how users browse the search result page. Most existing click models implicitly assume that all users are homogeneous and act in the same way when browsing the search results. However, a number of researches have shown that users have diverse behavioral patterns, which is also observed in this paper by eye-tracking experiments and click-through log analysis. As a uniform click model for all users can hardly capture the diverse click behavior, in this paper we incorporate user preferences into both a variety of existing click models and a novel click model. The experimental results on a large-scale click-through data set show consistent and significant performance improvement of the click models with user preferences integrated.