@inproceedings{ebf6adc1f8594b1b9073cf53e58c45fc,
title = "Personalized EDM Subject Generation via Co-factored User-Subject Embedding",
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.",
keywords = "Recommendation, Summarization, Tensor co-factorization",
author = "Chen, {Yu Hsiu} and Tam, {Zhi Rui} and Shuai, {Hong Han}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 ; Conference date: 07-05-2024 Through 10-05-2024",
year = "2024",
doi = "10.1007/978-981-97-2253-2_5",
language = "English",
isbn = "9789819722525",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "55--67",
editor = "De-Nian Yang and Xing Xie and Tseng, {Vincent S.} and Jian Pei and Jen-Wei Huang and Lin, {Jerry Chun-Wei}",
booktitle = "Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings",
address = "德國",
}