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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Banner Personalization for e-Commerce

Authors : Ioannis Maniadis, Konstantinos N. Vavliakis, Andreas L. Symeonidis

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

Real-time website personalization is a concept that is being discussed for more than a decade, but has only recently been applied in practice, according to new marketing trends. These trends emphasize on delivering user-specific content based on behavior and preferences. In this context, banner recommendation in the form of personalized ads is an approach that has attracted a lot of attention. Nevertheless, banner recommendation in terms of e-commerce main page sliders and static banners is even today an underestimated problem, as traditionally only large e-commerce stores deal with it. In this paper we propose an integrated framework for banner personalization in e-commerce that can be applied in small-medium e-retailers. Our approach combines topic-models and a neural network, in order to recommend and optimally rank available banners of an e-commerce store to each user separately. We evaluated our framework against a dataset from an active e-commerce store and show that it outperforms other popular approaches.

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Metadata
Title
Banner Personalization for e-Commerce
Authors
Ioannis Maniadis
Konstantinos N. Vavliakis
Andreas L. Symeonidis
Copyright Year
2019
Book
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-19823-7_53

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