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An on-demand service aggregation and service recommendation method based on RGPS

机译：基于RGPS的按需服务聚合与服务推荐方法

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"Internet plus" application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more ordered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method.
机译：“ Internet plus”应用程序服务推荐受到两个问题的挑战：一是服务量的增加和服务组织的混乱。第二是用户需求的多样化。这项研究的研究重点是研究如何实现更有序的聚合并推荐满足用户个性化需求的服务。本文解决了常规服务聚合的无序性，并考虑了具有非功能目标的QoS权重的聚合要求。基于使用角色（R），目标（G），过程（P），服务（S）需求元模型的语义相关性，提出了RGPS关联，它是用于有序QoS服务聚合的加权网络。然后，基于具有作用和目标后推的LSTM神经网络，使用RGPS关联网络，提供了一种个性化服务推荐方法，可以实现高质量的精确服务。最后，在旅游领域对服务推荐进行了仿真实验，验证了服务推荐方法的准确性，有效性和应用价值。

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