@inproceedings{wei_recommender_2021,
	address = {New York, NY, USA},
	title = {Recommender {Systems} for {Software} {Project} {Managers}},
	url = {https://doi.org/10.1145/3463274.3463951},
	doi = {10.1145/3463274.3463951},
	abstract = {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.},
	urldate = {2021-09-14},
	booktitle = {{EASE} 2021},
	publisher = {Association for Computing Machinery},
	author = {Wei, Liang and Capretz, Luiz Fernando},
	month = jun,
	year = {2021},
	note = {Journal Abbreviation: EASE 2021},
	keywords = {RSSE, Recommender Engine, Project Management, Recommendation System, Recommendation System in Software Engineering},
	pages = {412--417},
}

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