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Probabilistic graph model and neural network perspective of click models for web search

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Abstract

Click behavior is a typical user behavior in the web search. How to capture and model users’ click behavior has always been a common research topic. However, there are few review studies on this topic. In this paper, we present a survey to comprehensively analyze click models of web search via types of models. Based on differences in research hypotheses and modeling methods, click models are generally divided into probability graph-based click models, neural network-based click models, and hybrid click models. Firstly, we give a discussion of click models in the extant literature, within which basic assumptions and their extensions, advantages and disadvantages for click models are presented. We also compare and analyze the characteristics and application scenarios of different types of models. Secondly, we choose eight representative click models and conduct comparative experiments on two real-world session datasets to compare their performance. Finally, we identify current research trends, main challenges and potential future directions of click models worthy of further explorations.

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Availability of data and materials

The datasets we use are all public.

Notes

  1. https://trec.nist.gov/data/session2014.html.

  2. http://www.thuir.cn/tiangong-st/.

  3. https://github.com/markovi/PyClick.

  4. https://github.com/CHIANGEL/Neural-Click-Model.

  5. https://github.com/xuanyuan14/CACM-master.

  6. https://github.com/WwangYingFei.

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Acknowledgements

Not applicable.

Funding

This work was supported in part by the Key Scientific Research Project of North Minzu University titled “Study on Grape Cluster Detection and Leaf Disease Identification Based on Semi-Supervised Transfer Learning” under Grant 2023ZRLG12; in part by the Ministry of Education Industry-University Cooperation Synergistic Education Program titled “Teaching Content Reform of ‘Intelligent Recommendation System’ Based on Cloud Big Data Platform” under Grant 220802539112039; in part by the Starting Project of Scientific Research in the North Minzu University titled “Research of Information Retrieval Model Based on the Decision Process” under Grant 2020KYQD37.

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  1. Jian Wang, Meng Wang, Xintao Chu contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China

    Jianping Liu, Yingfei Wang, Meng Wang & Xintao Chu

  2. The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China

    Jianping Liu

  3. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Jian Wang

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J.L. and Y.W. wrote the main manuscript text, J.W. translated the full text, M.W. prepared figures, and X.C. prepared tables. All authors reviewed the manuscript.

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Correspondence to Yingfei Wang.

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Liu, J., Wang, Y., Wang, J. et al. Probabilistic graph model and neural network perspective of click models for web search. Knowl Inf Syst 66, 5829–5873 (2024). https://doi.org/10.1007/s10115-024-02145-z

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