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Open Access

Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210056, China
Faculty of Mathematics, University of Waterloo, Waterloo N2L 3G1, Canada
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Abstract

Edge computing platforms enable application developers and content providers to provide context-aware services (such as service recommendations) using real-time wireless access network information. How to recommend the most suitable candidate from these numerous available services is an urgent task. Click-through rate (CTR) prediction is a core task of traditional service recommendation. However, many existing service recommender systems do not exploit user mobility for prediction, particularly in an edge computing environment. In this paper, we propose a model named long and short-term user preferences modeling with a multi-interest network based on user behavior. It uses a logarithmic network to capture multiple interests in different fields, enriching the representations of user short-term preferences. In terms of long-term preferences, users’ comprehensive preferences are extracted in different periods and are fused using a nonlocal network. Extensive experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

Keywords

recommender systemlogarithmic networknonlocal network

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 86-98
DOI: 10.26599/TST.2022.9010054
Cite this article:
Yuan R, Meng S, Dou R, et al. Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing. Tsinghua Science and Technology, 2024, 29(1): 86-98. https://doi.org/10.26599/TST.2022.9010054

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Received: 07 September 2022
Revised: 16 October 2022
Accepted: 04 November 2022
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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