Utilizing Embeddings to Learn a Universal Customer Behavior Representation in E-Commerce

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Miguel Alves Gomes

Utilizing Embeddings to Learn a Universal Customer Behavior Representation in E-Commerce

Name: Utilizing Embeddings to Learn a Universal Customer Behavior Representation in E-Commerce
Price: 201.58 AUD
Availability: OutOfStock
ISBN: 9783658507800

Miguel Alves Gomes

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Paperback

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English

Springer Vieweg
25 February 2026

Social research & statistics; Occupational & industrial psychology; E-commerce: business aspects; Computing: general; Machine learning

E-commerce operates in a highly dynamic and competitive environment, where customer satisfaction is key to success. Delivering personalized experiences at scale requires systems capable of reliably modeling individual customer behavior while respecting privacy and data protection constraints such as the GDPR. This book proposes a universal, privacy-compliant customer representation that is task-agnostic and incrementally adaptable. A decoupled three-stage approach is introduced, combining self-supervised learning of customer embeddings from behavioral data with flexible downstream models for predicting customer intentions. Temporal extensions improve performance, particularly under sparse information conditions, while lifelong learning enables dynamic adaptation to new interactions and evolving product spaces without full retraining. Comprehensive experiments across multiple real-world e-commerce datasets demonstrate consistent performance improvements over state-of-the-art baselines. By decoupling personalization from personal data, this work offers a scalable and privacy-preserving foundation for next-generation personalization systems.

By: Miguel Alves Gomes
Imprint: Springer Vieweg
Country of Publication: Germany
Dimensions: Height: 210mm, Width: 148mm,
ISBN: 9783658507800
ISBN 10: 3658507802
Pages: 220
Publication Date: 25 February 2026
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Miguel Alves Gomes obtained his doctorate at the Chair of Technologies and Management of Digital Transformation. His research focuses on personalisation through artificial intelligence, particularly in the modelling of customer behaviour and the use of natural language processing in applications.