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Open Access

Extracting Relevant Terms from Mashup Descriptions for Service Recommendation

Department of Automation, Tsinghua University, Beijing 100084, China.
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Abstract

Due to the exploding growth in the number of web services, mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort. Service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates. However, the majority of existing methods utilize service profiles for content matching, not mashup descriptions. This makes them suffer from vocabulary gap and cold-start problem when recommending components for new mashups. In this paper, we propose a two-step approach to generate high-quality service representation from mashup descriptions. The first step employs a linear discriminant function to assign each term with a component service such that a coarse-grained service representation can be derived. In the second step, a novel probabilistic topic model is proposed to extract relevant terms from coarse-grained service representation. Finally, a score function is designed based on the final high-quality representation to determine recommendations. Experiments on a data set from ProgrammableWeb.com show that the proposed model significantly outperforms state-of-the-art methods.

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Tsinghua Science and Technology
Pages 293-302
Cite this article:
Zhong Y, Fan Y. Extracting Relevant Terms from Mashup Descriptions for Service Recommendation. Tsinghua Science and Technology, 2017, 22(3): 293-302. https://doi.org/10.23919/TST.2017.7914201

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Received: 07 May 2016
Revised: 28 September 2016
Accepted: 20 October 2016
Published: 04 May 2017
© The authors 2017
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