DLTSR: A Deep Learning Framework for Recommendations of Long-Tail Web Services
DLTSR: A Deep Learning Framework for Recommendations of Long-Tail Web Services
复制标题
DLTSR：用于长尾 Web 服务推荐的深度学习框架
DOI：
10.1109/tsc.2017.2681666
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发表时间：
2020-01-01
影响因子：
8.1
通讯作者：
Zhang, Jia
中科院分区：
文献类型：
--
作者：
Bai, Bing;Fan, Yushun;Zhang, Jia
随着web服务的日益普及，越来越多的开发人员将多个服务组合成mashup。开发人员对非流行服务（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的严重稀疏性和描述内容的质量不令人满意。在本文中，我们建议构建一个深度学习框架来解决这些挑战并执行准确的长尾建议。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）进行特征提取。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，我们学习开发人员偏好的模式，而不是对单个服务建模。我们在真实数据集上的实验结果表明，使用这种基于自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。
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.