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

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

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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.

recommender systemlogarithmic networknonlocal network

Rui Yuan、Shunmei Meng、Ruihan Dou、Xinna Wang

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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

Open Research Project of the State Key Laboratory of Novel Software Technology(Nanjing University)National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China2020 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of ChinaFundamental Research Fund for the Central UniversitiesFundamental Research Fund for the Central UniversitiesFundamental Research Fund for the Central UniversitiesPostdoctoral Science Foundation of China

KFKT2022B282020YFB18046046170226462076130618722193091801220430920041112309190112822019M651835

2024

10.26599/TST.2022.9010054

清华大学学报自然科学版(英文版)
清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI
影响因子：0.474
ISSN：1007-0214
年,卷(期)：2024.29(1)
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