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

Lavneet Singh, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

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
Original languageEnglish
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Subtitle of host publicationLecture Notes in Computer Science
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Place of PublicationTurkey
PublisherSpringer
Pages302-309
Number of pages8
Volume9489
ISBN (Electronic)9783319265322
ISBN (Print)9783319265322
DOIs
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing ICONIP 2015 - Istanbul, Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9489
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing ICONIP 2015
Abbreviated titleICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

Fingerprint

User Profiling
Extreme Learning Machine
RANSAC
Multi-model
Personalization
Electronic mail
Boosting
Electronic Mail
Pruning
Learning systems
Regression
Gradient
Customers
Customization
Architectural Design
User Profile
Architectural design
Experimental Validation
Electronic commerce
Electronic Commerce

Cite this

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 S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings: Lecture Notes in Computer Science (Vol. 9489, pp. 302-309). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489). Turkey: Springer. https://doi.org/10.1007/978-3-319-26532-2_33
Singh, Lavneet ; CHETTY, Girija. / Email Personalization and User Profiling Using RANSAC Multi Model Response Regression Based Optimized Pruning Extreme Learning Machines and Gradient Boosting Trees. Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings: Lecture Notes in Computer Science. editor / Sabri Arik ; Tingwen Huang ; Weng Kin Lai ; Qingshan Liu. Vol. 9489 Turkey : Springer, 2015. pp. 302-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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 S Arik, T Huang, WK Lai & Q Liu (eds), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings: Lecture Notes in Computer Science. vol. 9489, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9489, Springer, Turkey, pp. 302-309, 22nd International Conference on Neural Information Processing ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26532-2_33

Email Personalization and User Profiling Using RANSAC Multi Model Response Regression Based Optimized Pruning Extreme Learning Machines and Gradient Boosting Trees. / Singh, Lavneet; CHETTY, Girija.

Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings: Lecture Notes in Computer Science. ed. / Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. Vol. 9489 Turkey : Springer, 2015. p. 302-309 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Singh L, CHETTY G. 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 WK, Liu Q, editors, Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings: Lecture Notes in Computer Science. Vol. 9489. Turkey: Springer. 2015. p. 302-309. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26532-2_33