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Email Personalization and User Profiling Using RANSAC Multi Model Response Regression Based Optimized Pruning Extreme Learning Machines and Gradient Boosting Trees

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Book cover Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

Email personalization is the process of customizing the content and structure of email according to member’s specific and individual needs taking advantage of member’s navigational behavior. Personalization is a refined version of customization, where marketing is done automated on behalf of customer’s user’s profiles, rather than customer requests on his own behalf. There is very thin line between customization and personalization which is achieved by leveraging customer level information using analytical tools. E-commerce is growing fast, and with this growth companies are willing to spend more on improving the online experience.

Thus, in this study, we propose a new architectural design of email personalization and user profiling using gradient boost trees and optimized pruned extreme learning machines as base estimators. We also conducted an in-depth data analysis to find each member’s behavior and important attributes which plays a significant role in increasing click rates in personalized emails. From the experimental validation, we concluded that our prosed method works much better in predicting customer’s behavior on deals send in personalized emails compared to other methods in past literature.

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Author information

Authors and Affiliations

  1. Faculty of ESTEM, University of Canberra, Canberra, Australia

    Lavneet Singh & Girija Chetty

Authors
  1. Lavneet Singh
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  2. Girija Chetty
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Corresponding author

Correspondence to Lavneet Singh .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Singh, L., Chetty, G. (2015). Email Personalization and User Profiling Using RANSAC Multi Model Response Regression Based Optimized Pruning Extreme Learning Machines and Gradient Boosting Trees. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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