Radin Hamidi Rad, A. Mitha, Hossein Fani, M. Kargar, Jaroslaw Szlichta, E. Bagheri
{"title":"PyTFL","authors":"Radin Hamidi Rad, A. Mitha, Hossein Fani, M. Kargar, Jaroslaw Szlichta, E. Bagheri","doi":"10.1145/3459637.3481992","DOIUrl":null,"url":null,"abstract":"We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3459637.3481992","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.