@article{Zhong2017, 
author = {Yang Zhong and Yushun Fan},
title = {Extracting Relevant Terms from Mashup Descriptions for Service Recommendation},
year = {2017},
journal = {Tsinghua Science and Technology},
volume = {22},
number = {3},
pages = {293-302},
keywords = {service recommendation, topic model, mashup descriptions, linear discriminant function},
url = {https://www.sciopen.com/article/10.23919/TST.2017.7914201},
doi = {10.23919/TST.2017.7914201},
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.}
}