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 m...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.展开更多
基金This paper was partially supported by the Open Research Project of the State Key Laboratory of Novel Software Technology(Nanjing University)(No.KFKT2022B28)National Key R&D Program of China(No.2020YFB1804604)+2 种基金the National Natural Science Foundation of China(Nos.61702264,62076130,and 61872219)the 2020 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of China,the Fundamental Research Fund for the Central Universities(Nos.30918012204,30920041112,and 30919011282)the Postdoctoral Science Foundation of China(No.2019M651835).
文摘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.
