The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.
翻译:建议系统的设计以复杂的信息处理和大数据互动为基础,这种个性化观点在过去十年中演变成为一个热点领域,应用可能证明有助于解决软件开发领域的问题,因此,随着软件工程建议系统(RSSE)的发展,软件项目与利益攸关方的协调正在改善,这一试验审查了四个开放源码建议系统,并采用了一个定制的推荐引擎,有两个面向工业的软件包:Lenskit和Mahout,对其中每一项主要功能进行了审查,并在试验期间查明了问题。