基于协作网络的开源领域专家检索模型

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Big Data Pub Date : 2025-01-01 DOI:10.1109/TBDATA.2024.3524829

Qingxi Peng;Zhenjie Weng;Wei Wang;Xinyi Wang;Lan You

{"title":"基于协作网络的开源领域专家检索模型","authors":"Qingxi Peng;Zhenjie Weng;Wei Wang;Xinyi Wang;Lan You","doi":"10.1109/TBDATA.2024.3524829","DOIUrl":null,"url":null,"abstract":"Aiming at the problem that the GitHub platform only supports the retrieval of developers through their usernames and it is difficult to directly obtain developers' expertise information, this paper proposes an open source domain expert retrieval model (OSDERM) based on the network representation learning algorithm OSC2vec (Open Source Collaboration to Vector). The model mainly consists of two core parts: Expert Profiling and Expert Finding. Expert Profiling aims to enrich the expertise information in the search results by labeling the expertise of developers; while Expert Finding achieves rapid location of the most suitable domain experts through keyword matching, which greatly saves the time and effort of searching for experts in the open source community. Experiments using the GitHub ecological dataset show that the model outperforms existing comparative algorithms in discovering open source domain experts, and can provide an effective reference for enterprise recruitment","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1720-1732"},"PeriodicalIF":5.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Collaborative Network-Based Retrieval Model for Open Source Domain Experts\",\"authors\":\"Qingxi Peng;Zhenjie Weng;Wei Wang;Xinyi Wang;Lan You\",\"doi\":\"10.1109/TBDATA.2024.3524829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem that the GitHub platform only supports the retrieval of developers through their usernames and it is difficult to directly obtain developers' expertise information, this paper proposes an open source domain expert retrieval model (OSDERM) based on the network representation learning algorithm OSC2vec (Open Source Collaboration to Vector). The model mainly consists of two core parts: Expert Profiling and Expert Finding. Expert Profiling aims to enrich the expertise information in the search results by labeling the expertise of developers; while Expert Finding achieves rapid location of the most suitable domain experts through keyword matching, which greatly saves the time and effort of searching for experts in the open source community. Experiments using the GitHub ecological dataset show that the model outperforms existing comparative algorithms in discovering open source domain experts, and can provide an effective reference for enterprise recruitment\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"11 4\",\"pages\":\"1720-1732\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10819979/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10819979/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

针对GitHub平台仅支持通过用户名检索开发人员，难以直接获取开发人员专业知识信息的问题，本文提出了一种基于网络表示学习算法OSC2vec （open source Collaboration to Vector）的开源领域专家检索模型（OSDERM）。该模型主要由专家分析和专家发现两个核心部分组成。专家分析的目的是通过标注开发人员的专业知识来丰富搜索结果中的专业知识信息；而Expert Finding则通过关键字匹配快速定位到最适合的领域专家，大大节省了在开源社区中搜索专家的时间和精力。使用GitHub生态数据集进行的实验表明，该模型在发现开源领域专家方面优于现有的比较算法，可以为企业招聘提供有效的参考

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Collaborative Network-Based Retrieval Model for Open Source Domain Experts

Aiming at the problem that the GitHub platform only supports the retrieval of developers through their usernames and it is difficult to directly obtain developers' expertise information, this paper proposes an open source domain expert retrieval model (OSDERM) based on the network representation learning algorithm OSC2vec (Open Source Collaboration to Vector). The model mainly consists of two core parts: Expert Profiling and Expert Finding. Expert Profiling aims to enrich the expertise information in the search results by labeling the expertise of developers; while Expert Finding achieves rapid location of the most suitable domain experts through keyword matching, which greatly saves the time and effort of searching for experts in the open source community. Experiments using the GitHub ecological dataset show that the model outperforms existing comparative algorithms in discovering open source domain experts, and can provide an effective reference for enterprise recruitment

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Big Data Multiple-

CiteScore

11.80

自引率

2.80%

发文量

114

期刊介绍： The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.