Privacy-Preserving User Modeling for Digital Marketing Campaigns: The Case of a Data Monetization Platform

Carolina Lucas (), Emila Aguiar (), Patrícia Macedo (), Zhenze Wu () and Qiwei Han ()
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Carolina Lucas: Nova School of Business and Economics
Emila Aguiar: Nova School of Business and Economics
Patrícia Macedo: Nova School of Business and Economics
Zhenze Wu: Nova School of Business and Economics
Qiwei Han: Nova School of Business and Economics

A chapter in Advances in Digital Marketing and eCommerce, 2022, pp 171-179 from Springer

Abstract: Abstract This work proposes using a collection of Deep Learning approaches to design the privacy-preserving data monetization platform to improve the digital marketplace where users have control over their data and marketers can identify the right users for their marketing campaigns through several steps. First, representation learning on hyperbolic space is performed to learn latent embeddings of user interests across multiple data sources with hierarchical structures. Second, Generative Adversarial Networks is performed to generate synthetic user interests from the embeddings. Third, the system adopts a Federated Learning technique to ensure the user modeling is trained in a decentralized manner on the user's own devices while keeping data localized without sharing with marketers. Last, a recommender system is built upon the learned user interests to identify the right users for digital marketing campaigns. Overall, this work provides a holistic solution for privacy-preserving user modeling for digital marketing campaigns.

Keywords: Deep learning; Digital marketing; Data monetization; Data privacy; Hyperbolic embeddings; Federated Learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-05728-1_20

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DOI: 10.1007/978-3-031-05728-1_20

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