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Wisdom Web of Things pp 181–207Cite as

Leveraging Neurodata to Support Web User Behavior Analysis

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Abstract

Given its complexity, understanding the behavior of users on the Web has been one of the most challenging tasks for data mining-related fields. Historically, most of the approaches have considered web logs as the main source of data. This has led to several successful cases, both in industry and academia, but has also presented several issues and limitations. Given the new challenges and the need for personalization, improvement is required in the overall understanding of the processes that lie behind web browsing decision making. The use of neurodata to support this analysis represents a huge opportunity in terms of understanding the actions taken by the user on the web in a more comprehensive way. Techniques such as eye tracking, pupil dilation and EEG analysis could provide valuable information to craft more robust models. This chapter overviews the current state of the art of the use of neurodata for web-based analysis, providing a description and analysis in terms of the feasibility and effectiveness of each strategy given a specific problem.

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Acknowledgments

The authors would like to acknowledge the continuous support of the Chilean Millennium Institute of Complex Engineering Systems (ICM: P-05-004-F, CONICYT: FBO16), the Fondecyt Project 1160117, and the FONDEF-CONICYT CA12I10061 - AKORI project.

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Authors and Affiliations

  1. Web Intelligence Centre, Industrial Engineering Department, University of Chile, Santiago, Chile

    Pablo Loyola, Gustavo Martinez & Juan D. Velásquez

  2. Neurosystems Laboratory, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile

    Enzo Brunetti & Pedro Maldonado

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  1. Pablo Loyola
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  4. Juan D. Velásquez
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  5. Pedro Maldonado
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Correspondence to Juan D. Velásquez .

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Editors and Affiliations

  1. Dep of Life Sci & Informatic, Maebashi Institute of Technology Dep of Life Sci & Informatic, Maebashi, Japan

    Ning Zhong

  2. Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan

    Jianhua Ma

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  4. Faculty of Computer and Information Science, Hosei University , Tokyo, Japan

    Runhe Huang

  5. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, Queensland, Australia

    Xiaohui Tao

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Loyola, P., Brunetti, E., Martinez, G., Velásquez, J.D., Maldonado, P. (2016). Leveraging Neurodata to Support Web User Behavior Analysis. In: Zhong, N., Ma, J., Liu, J., Huang, R., Tao, X. (eds) Wisdom Web of Things. Web Information Systems Engineering and Internet Technologies Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-44198-6_8

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