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

Rui Yuan ¹Shunmei Meng ¹Ruihan Dou ²Xinna Wang¹

扫码查看

作者信息

1. Department of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210056,China
2. 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
折叠

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.

Key words

recommender system/logarithmic network/nonlocal network

引用本文复制引用

基金项目

Open Research Project of the State Key Laboratory of Novel Software Technology(Nanjing University)(KFKT2022B28)

National Key R&D Program of China(2020YFB1804604)

National Natural Science Foundation of China(61702264)

National Natural Science Foundation of China(62076130)

National Natural Science Foundation of China(61872219)

2020 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of China()

Fundamental Research Fund for the Central Universities(30918012204)

Fundamental Research Fund for the Central Universities(30920041112)

Fundamental Research Fund for the Central Universities(30919011282)

Postdoctoral Science Foundation of China(2019M651835)

出版年

2024

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

参考文献量50

段落导航