  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.
