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

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14646))

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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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Notes

  1. 1.

    https://www.statista.com/statistics/812060/email-marketing-revenue-worldwide/.

  2. 2.

    http://bit.ly/2kc7OD7.

  3. 3.

    https://lucene.apache.org.

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Author information

Authors and Affiliations

  1. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Yu-Hsiu Chen, Zhi Rui Tam & Hong-Han Shuai

Authors
  1. Yu-Hsiu Chen
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  2. Zhi Rui Tam
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  3. Hong-Han Shuai
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Corresponding author

Correspondence to Hong-Han Shuai .

Editor information

Editors and Affiliations

  1. Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, YH., Tam, Z.R., Shuai, HH. (2024). Personalized EDM Subject Generation via Co-factored User-Subject Embedding. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14646. Springer, Singapore. https://doi.org/10.1007/978-981-97-2253-2_5

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  • DOI: https://doi.org/10.1007/978-981-97-2253-2_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2252-5

  • Online ISBN: 978-981-97-2253-2

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