With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity of historical usage data and unsatisfactory quality of description content. In this paper, we propose to build a deep learning framework to address these challenges and perform accurate long-tail recommendations. To tackle the problem of unsatisfactory quality of description content, we use stacked denoising autoencoders (SDAE) to perform feature extraction. Additionally, we impose the usage records in hot services as a regularization of the encoding output of SDAE, to provide feedback to content extraction. To address the sparsity of historical usage data, we learn the patterns of developers’ preference instead of modeling individual services. Our experimental results on a real-world dataset demonstrate that, with such joint autoencoder based feature representation and content-usage learning framework, the proposed algorithm outperforms the state-of-the-art baselines significantly.

中文翻译：

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DLTSR：推荐长尾 Web 服务的深度学习框架

随着 Web 服务的日益流行，越来越多的开发人员正在将多个服务组合到 mashup 中。开发人员对不受欢迎的服务（即长尾服务）表现出越来越大的兴趣，然而，试图解决长尾 Web 服务推荐问题的研究非常少。准确推荐长尾服务的主要挑战包括历史使用数据严重稀疏和描述内容质量不理想。在本文中，我们建议构建一个深度学习框架来应对这些挑战并执行准确的长尾推荐。为了解决描述内容质量不理想的问题，我们使用堆叠去噪自编码器（SDAE）来执行特征提取。此外，我们将热服务中的使用记录作为 SDAE 编码输出的正则化，为内容提取提供反馈。为了解决历史使用数据的稀疏性，我们学习了开发人员偏好的模式，而不是对单个服务进行建模。我们在真实世界数据集上的实验结果表明，通过这种基于联合自动编码器的特征表示和内容使用学习框架，所提出的算法显着优于最先进的基线。