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Open Access

Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence

Graduate School, Angeles University Foundation, Angeles City 2009, Philippines
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China
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Abstract

Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence. Recently, deep neural network-based sequential recommendation models gained considerable attention. However, existing approaches pay little attention to users’ dynamically evolving interests, which are influenced by product attributes, especially product category. To overcome these challenges, we propose a dynamic personalized recommendation model: DynaPR. Specifically, we first embed product information and attribute information into a unified data space. Then, we exploit long short-term memory (LSTM) networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks. Finally, similarity values between users are measured through pairwise interest features, and personalized recommendation lists are generated. A series of experiments reveal the superiority of the proposed method compared with other advanced methods.

Keywords

embeddingLSTM networkspersonalized recommendationsevolving interests

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 185-196
DOI: 10.26599/TST.2023.9010025
Cite this article:
Yang X, Esquivel JA. Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence. Tsinghua Science and Technology, 2024, 29(1): 185-196. https://doi.org/10.26599/TST.2023.9010025

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Received: 22 February 2023
Revised: 20 March 2023
Accepted: 30 March 2023
Published: 21 August 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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