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Advancing sustainable marketing through empowering recommendation: a deep learning approach

Zhong, Ziqi ORCID: 0000-0002-3919-9999 and Yue, Linhong (2024) Advancing sustainable marketing through empowering recommendation: a deep learning approach. In: Proceedings of 2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS). Digital Society and Intelligent Systems (DSInS), International Conference on (2023). IEEE, 353 - 356. ISBN 9798350331387

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Identification Number: 10.1109/DSInS60115.2023.10455137


para> In the contemporary era, online shopping has become the preferred mode of retail for consumers. Addressing users' demands for personalized products while simultaneously promoting sustainable marketing practices is of paramount importance for major e-commerce platforms. This paper explores the integration of deep learning techniques into recommendation systems, focusing on the Inception structural neural network (NCF-i), to enhance prediction accuracy and operational efficiency. We also introduce sustainable marketing concepts into the context of personalized recommendations. To achieve this, we design a pairwise self-encoder that improves the content-aware recommendation algorithm for sustainable and personalized products, leveraging the gate attention mechanism. Experimental results demonstrate that our proposed recommendation system not only outperforms current mainstream models in terms of prediction accuracy and stability but also fosters sustainable marketing practices, showcasing its effectiveness and broad applicability.

Item Type: Book Section
Official URL:
Additional Information: © 2023 IEEE
Divisions: Management
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 21 May 2024 09:09
Last Modified: 11 Jun 2024 22:54

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