NDLI: Learning click models via probit bayesian inference

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Learning click models via probit bayesian inference

Content Provider	ACM Digital Library
Author	Zhang, Yuchen Wang, Dong Zhang, Li Zhang, Zhihua Hu, Botao Wang, Gang Chen, Weizhu
Abstract	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.
Starting Page	439
Ending Page	448
Page Count	10
File Format	PDF
ISBN	9781450300995
DOI	10.1145/1871437.1871496
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Probit bayesian inference Click model Click log analysis
Content Type	Text
Resource Type	Article

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