As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new “user” to a service recommender system. To address this new “user” cold-start problem, we propose a multiplex interaction-oriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics.

中文翻译：

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用于冷启动服务推荐的具有多重交互的深度神经网络

随着面向服务的计算（SOC）技术逐渐成熟，开发基于服务的系统（如混搭）近年来变得越来越流行。面对快速增长的Web服务数量，按需为开发者推荐合适的组件服务是mashup开发中的一个至关重要的问题。特别是，由于要开发的新混搭不包含组件服务，因此它是服务推荐系统的新“用户”。为了解决这个新的“用户”冷启动问题，我们提出了一种名为 MISR 的面向多重交互的服务推荐方法，它将服务和 mashup 之间的三种类型的交互合并到一个深度神经网络中。在本文中，我们利用深度学习提供的强大表示学习能力，从混搭和服务之间的各种类型的交互中提取隐藏的结构和特征。在 ProgrammableWeb 的真实世界数据集上进行的实验表明，MISR 在常用评估指标方面优于几种最先进的方法。