Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users’ Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users’ service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

中文翻译：

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多接入边缘计算中基于双边感知的准确可靠服务推荐


多接入边缘计算（MEC）是一种新兴的计算范式，它将服务从集中式云带到附近的网络边缘，以提高用户的体验质量（QoE）。随着 MEC 中提供具有动态服务质量 (QoS) 的海量服务，用户找到满足其需求的可靠服务变得具有挑战性。因此，MEC迫切需要服务推荐技术。尽管现有的服务推荐方法可以很好地推荐用户可能感兴趣的热门服务，但它们无法在MEC环境中推荐具有可靠QoS的服务。为了解决这个问题，提出了一种基于双边感知的准确可靠的服务推荐（ARSR）方法，旨在通过感知用户的服务需求和候选服务的多QoS来主动推荐可靠的服务。 ARSR 包括三个主要步骤。首先，通过基于改进的在线深度学习模型的上下文感知服务需求预测方法来估计用户的服务需求。然后，通过基于改进的多任务深度神经网络的多维上下文感知QoS预测方法来预测候选服务的多个QoS属性。最后根据预测的QoS向用户推荐最优的服务。已经进行了大量的实验来验证所提出的方法并证明其性能优越性。