{"title":"基于pagerank的协同设计团队成员影响研究","authors":"Xue Mengjia, J. Yang","doi":"10.1109/ISET55194.2022.00012","DOIUrl":null,"url":null,"abstract":"Aiming at the lack of evaluation indicators of the influence of collaborative design team members, this paper proposes a collaborative design team member influence algorithm DesignRank that combines collaborative design behavior characteristics with PageRank. The DesignRank algorithm takes into account the two factors of self-design level and interactor's design level, and calculates the influence of group members in the collaborative design platform. The experimental results show that the DesignRank algorithm is a more comprehensive and complex method.","PeriodicalId":365516,"journal":{"name":"2022 International Symposium on Educational Technology (ISET)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A PageRank-based Collaborative Design Team Member Influence Study\",\"authors\":\"Xue Mengjia, J. Yang\",\"doi\":\"10.1109/ISET55194.2022.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the lack of evaluation indicators of the influence of collaborative design team members, this paper proposes a collaborative design team member influence algorithm DesignRank that combines collaborative design behavior characteristics with PageRank. The DesignRank algorithm takes into account the two factors of self-design level and interactor's design level, and calculates the influence of group members in the collaborative design platform. The experimental results show that the DesignRank algorithm is a more comprehensive and complex method.\",\"PeriodicalId\":365516,\"journal\":{\"name\":\"2022 International Symposium on Educational Technology (ISET)\",\"volume\":\"218 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Symposium on Educational Technology (ISET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISET55194.2022.00012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Symposium on Educational Technology (ISET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISET55194.2022.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A PageRank-based Collaborative Design Team Member Influence Study
Aiming at the lack of evaluation indicators of the influence of collaborative design team members, this paper proposes a collaborative design team member influence algorithm DesignRank that combines collaborative design behavior characteristics with PageRank. The DesignRank algorithm takes into account the two factors of self-design level and interactor's design level, and calculates the influence of group members in the collaborative design platform. The experimental results show that the DesignRank algorithm is a more comprehensive and complex method.