通过结合增强的递阶Dirichlet过程和分解机进行移动服务推荐,Mobile Information Systems

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通过结合增强的递阶Dirichlet过程和分解机进行移动服务推荐
Mobile Information Systems Pub Date : 2019-03-25 , DOI: 10.1155/2019/6423805
Buqing Cao ₁ , Bing Li ₂ , Jianxun Liu ₁ , Mingdong Tang ₃ , Yizhi Liu ₁ , Yanxinwen Li ₄

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最近，Mashup正在成为移动服务计算环境中一种有前途的软件开发方法，该方法使软件开发人员能够组合现有的移动服务来创建新的或增值的复合RESTful Web应用程序。由于Internet上移动服务的迅速增加，很难找到最合适的服务来构建用户所需的Mashup应用程序。在本文中，我们集成了词嵌入增强的分层Dirichlet流程和分解机器，以推荐移动服务来构建高质量的Mashup应用程序。首先，这种方法使用Word2vec工具扩展了Mashup应用程序和移动服务的描述文档，并利用分层Dirichlet过程从Mashup和移动服务的扩展描述文档中衍生了潜在主题。其次，将分解机用于训练这些潜在主题，以预测Mashup调用移动服务的可能性，并为Mashup开发推荐高质量的移动服务。最后，对该方法的性能进行了综合评价。实验结果表明，与现有推荐方法相比，该方法在MAE和RMSE方面有显着改进。分解机用于训练这些潜在主题，以预测Mashup调用移动服务的可能性，并为Mashup开发推荐高质量的移动服务。最后，对该方法的性能进行了综合评价。实验结果表明，与现有推荐方法相比，该方法在MAE和RMSE方面有显着改进。分解机用于训练这些潜在主题，以预测Mashup调用移动服务的可能性，并为Mashup开发推荐高质量的移动服务。最后，对该方法的性能进行了综合评价。实验结果表明，与现有推荐方法相比，该方法在MAE和RMSE方面有显着改进。

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Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Recently, Mashup is becoming a promising software development method in the mobile service computing environment, which enables software developers to compose existing mobile services to create new or value-added composite RESTful web application. Due to the rapid increment of mobile services on the Internet, it is difficult to find the most suitable services for building user-desired Mashup application. In this paper, we integrate word embeddings enhanced hierarchical Dirichlet process and factorization machines to recommend mobile services to build high-quality Mashup application. This method, first of all, extends the description documents of Mashup applications and mobile services by using Word2vec tool and derives latent topics from the extended description documents of Mashup and mobile services by exploiting the hierarchical Dirichlet process. Secondly, the factorization machine is applied to train these latent topics to predict the probability of mobile services invoked by Mashup and recommend mobile services with high-quality for Mashup development. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results indicate that compared with the existing recommendation methods, the proposed method has significant improvements in MAE and RMSE.

更新日期：2019-03-25

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