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User behavior modeling for better Web search ranking

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Abstract

Modern search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve clickthrough rate (CTR), Web search ranking, and query recommendations and suggestions. Although click-through logs can provide implicit feedback of users’ click preferences, deriving accurate absolute relevance judgments is difficult because of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are biased toward many aspects such as “position” (user’s attention decreases from top to bottom) and “trust” (Web site reputations will affect user’s judgment). To address these problems, researchers have proposed several behavior models (usually referred to as click models) to describe users? practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for building click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses parameters from traditional click models to restrict the meaning of some outputs in our model’s hidden layer. Experimental results show that the proposed model can achieve considerable improvement over state-of-the-art click models based on the evaluation metric of click perplexity.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61622208, 61732008, 61532011). It is also partly supported by Tsinghua University Initiative Scientific Research Program (2014Z21032) and the National Key Basic Research Program of China (973 Program) (2015CB358700).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Yiqun Liu, Chao Wang, Min Zhang & Shaoping Ma

Authors
  1. Yiqun Liu
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  2. Chao Wang
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  3. Min Zhang
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  4. Shaoping Ma
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Corresponding author

Correspondence to Yiqun Liu.

Additional information

Yiqun Liu is an associate professor at the Department of Computer Science and Technology, Tsinghua University, China. His major research interests are in Web search, user behavior analysis, and natural language processing. He serves as co- Editor-in-chief of Frontiers and Trends of Information Retrieval (FnTIR), Program Co-chair of SIGIR2018, Short Paper Co-chair of SIGIR2017, Program Co-chair of NTCIR-13, General Co-chair of AIRS2016 as well as (senior) program committee members of a number of important international academic conferences including SIGIR, WWW, AAAI, ACL and IJCAI. In 2016, he was supported by NSFC as an Outstanding Young Scholar (2017–2019).

Chao Wang is now working as a researcher at Baidu.com since he obtained his PhD degree from TsinghuaUniversity, China in 2016. His major research interests are in Web search and user behavior analysis. He has published a number of high quality papers in top-tier academic conference and journals such as SIGIR, CIKM and TOIS. He also received the best paper honorable mention award of SIGIR2015

Min Zhang is an associate professor in the Department of Computer Science and Technology, Tsinghua University, China. She specializes in Web search and recommendation and Web user modeling. Currently she is also the vice director of State Key Lab. of Intelligent Technology and Systems, the executive director of Tsinghua University-Microsoft Research Asia Joint Research Lab on Media and Search. She also serves as associate editor for the ACM Transaction on Information Systems (TOIS), Program co-Chair ofWSDM2017 and AIRS 2016, area chairs or senior PC members at SIGIR, CIKM, and PC members at WWW, IJCAI, KDD, AAAI, ACL, etc. She has published more than 70 papers in important international journals and conferences, and 12 of her patents are filed. She was awarded Beijing Science and Technology Award (First Prize) in 2016.

Shaoping Ma is a professor in the Department of Computer Science and Technology, Tsinghua University, China. He is also the director of "Tsinghua-Sogou" Joint Lab on Web search technology research, acting vice director of “Tiangong” Research Institute of Intelligent Computing of Tsinghua University and vice president of CAAI. He is interested in the research areas of intelligent information processing, information retrieval, and Web data mining. He has been recently focusing on improving search performance with the help of semantic mining of user behaviors.

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Liu, Y., Wang, C., Zhang, M. et al. User behavior modeling for better Web search ranking. Front. Comput. Sci. 11, 923–936 (2017). https://doi.org/10.1007/s11704-017-6518-6

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