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2024 | OriginalPaper | Buchkapitel

Personalized EDM Subject Generation via Co-factored User-Subject Embedding

verfasst von : Yu-Hsiu Chen, Zhi Rui Tam, Hong-Han Shuai

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

This paper introduces the Co-Factored User-Subject Embedding based Personalized EDM Subject Generation Framework (COUPES), a model for creating personalized Electronic Direct Mail (EDM) subjects. COUPES adapts to individual content and style preferences using a dual-encoder structure to process product descriptions and template features. It employs a soft template-based selective encoder and matrix co-factorization for nuanced user embeddings. Experiments show that COUPES excels in generating engaging, personalized subjects and reconstructing recommendation ratings, proving its effectiveness in personalized marketing and recommendation systems.

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Nächstes Kapitel Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting

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Titel: Personalized EDM Subject Generation via Co-factored User-Subject Embedding
verfasst von: Yu-Hsiu Chen
Zhi Rui Tam
Hong-Han Shuai
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-52-5

Electronic ISBN: 978-981-9722-53-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2253-2_5

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