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Banner Personalization for e-Commerce

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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Submitted on : Thursday, October 24, 2019 - 12:50:31 PM
Last modification on : Thursday, October 24, 2019 - 12:54:43 PM
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Ioannis Maniadis, Konstantinos Vavliakis, Andreas Symeonidis. Banner Personalization for e-Commerce. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.635-646, ⟨10.1007/978-3-030-19823-7_53⟩. ⟨hal-02331309⟩

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