Learning click models via probit bayesian inference

  • Authors:
  • Yuchen Zhang;Dong Wang;Gang Wang;Weizhu Chen;Zhihua Zhang;Botao Hu;Li Zhang

  • 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;Zhejiang University, Hangzhou, China;Tsinghua University and Microsoft Research Asia, Beijing, China;Tsinghua University, Beijing, China

  • Venue:
  • CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
  • Year:
  • 2010

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Recent advances in click models have positioned them as an effective approach to the improvement of interpreting click data, and some typical works include UBM, DBN, CCM, etc. After formulating the knowledge of user search behavior into a set of model assumptions, each click model developed an inference method to estimate its parameters. The inference method plays a critical role in terms of accuracy in interpreting clicks, and we observe that different inference methods for a click model can lead to significant accuracy differences. In this paper, we propose a novel Bayesian inference approach for click models. This approach regards click model under a unified framework, which has the following characteristics and advantages: 1. This approach can be widely applied to existing click models, and we demonstrate how to infer DBN, CCM and UBM through it. This novel inference method is based on the Bayesian framework which is more flexible in characterizing the uncertainty in clicks and brings higher generalization abilities. As a result, it not only excels in the inference methods originally developed in click models, but also provides a valid comparison among different models; 2. In contrast to the previous click models, which are exclusively designed for the position-bias, this approach is capable of capturing more sophisticated information such as BM25 and PageRank score into click models. This makes these models interpret click-through data more accurately. Experimental results illustrate that the click models integrated with more information can achieve significantly better performance on click perplexity and search ranking; 3. Because of the incremental nature of the Bayesian learning, this approach is scalable to process large scale and constantly growing log data.