下一步是什么?服务组合推荐的生成方法

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Guodong Fan, Shizhan Chen, Hongyue Wu, Ming Zhu, Xiao Xue, Zhiyong Feng
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引用次数: 0

摘要

服务推荐在创建组合服务、工作流、电子商务解决方案等方面非常重要。开发人员通常要花很长时间才能弄清楚下一个服务是什么。许多研究人员使用基于协作过滤或基于内容的方法向开发人员推荐服务。然而,由于无法对服务之间的共现关系进行建模,当前的方法无法为服务组合推荐下一个服务。这将导致服务组合建议的准确性降低。为了解决这一问题,本文提出了一种基于编码器-解码器的推荐器——EDeR,将服务推荐问题转化为生成问题。首先,我们采用自监督图嵌入方法,根据结构信息和描述信息充分学习每个服务的表示。然后,基于服务之间的共现关系,我们提出了一个编码器-解码器模型,以一种将用户需求转换为组合服务的方式顺序推荐服务。在真实数据集上进行的实验结果表明,EDeR的性能明显优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What Is Next? A Generative Approach for Service Composition Recommendations
Service recommendation is important in creating composite services, workflows, e-business solutions, etc. It often takes developers a long time to Figure out what the next service is. A lot of researchers have used collaborative filtering-based or content-based approaches to recommend services for developers. However, failing to model the co-occurrence relationships between services, current approaches cannot recommend the next services for service composition. This leads to a decrease in the accuracy of service composition recommendations. To tackle this problem, this paper proposes an Encoder-Decoder-based Recommender named EDeR, which transforms the service recommendation problem into a generation problem. First, we employ a self-supervised graph embedding method to fully learn the representation of each service according to both structural and descriptive information. Then, based on the co-occurrence relationships between services, we propose an encoder-decoder model to sequentially recommend services in a way that translates user requirements into a composite service. The results obtained from experiments conducted on a real-world dataset show that EDeR outperforms the state-of-the-art approaches significantly.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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