工业工程 ›› 2023, Vol. 26 ›› Issue (4): 9-15.doi: 10.3969/j.issn.1007-7375.2023.04.002

• 系统分析与管理决策 • 上一篇    下一篇

面向众包平台的偏好和结构相似度融合式设计团队发现

刘电霆1,2, 吴珊1, 赵思佳1, 尚磊1, 叶恒舟2   

  1. 1. 桂林理工大学 机械与控制工程学院;
    2. 信息科学与工程学院,广西 桂林 541004
  • 收稿日期:2022-06-06 发布日期:2023-09-08
  • 通讯作者: 吴珊(1997-),女,广西壮族自治区人,硕士研究生,主要研究方向为群组推荐系统。E-mail:1627911863@qq.com E-mail:1627911863@qq.com
  • 作者简介:刘电霆(1966-),男,江西省人,教授,博士,主要研究方向为网络+数据驱动设计制造、系统建模与测控
  • 基金资助:
    国家自然科学基金资助项目 (71961005);广西自然科学基金资助项目 (2020GXNSFAA297024)

A Method of Designer Team Discovery Integrating Preference and Structure Similarities for Crowdsourcing Platforms

LIU Dianting1,2, WU Shan1, ZHAO Sijia1, SHANG Lei1, YE Hengzhou2   

  1. 1. College of Mechanical and Control Engineering;
    2. College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
  • Received:2022-06-06 Published:2023-09-08

摘要: 在复杂产品的众包设计项目中,往往需要设计人员组成团队,不断交互与协作地完成相关任务。为了解决在组建团队时成员偏好不一的问题,提出一种基于成员偏好相似度和结构相似度相结合的团队发现算法S_Louvain,考虑了团队成员之间的偏好并改进了模块度指标。计算节点的偏好属性相似度和拓扑结构相似度,结合用户给定的节点及其邻居节点,综合考虑其偏好与结构相似性,扩展得到目标团队的候选节点集。以候选节点集为核心,挖掘设计团队的兴趣偏好来计算改进的模块度,并更新优化团队划分。在公开数据集和众包工程实例数据集上的实验结果表明,团队划分的模块度指标得到提高,验证了所提算法的可行性和实用性。

关键词: 众包设计, 用户偏好, 相似度, 模块度

Abstract: In crowdsourcing design projects of complex products, designers are often required to form teams to continuously interact and collaborate to complete relevant tasks. In order to solve the problem of different preferences among team members during team formation, this paper proposes a team discovery algorithm called S_Louvain based on the combination of member preference and structure similarities, which considers the preferences among team members and improves the modularity index. The preference attribute similarity and the topology structure similarity of nodes are first calculated. The candidate node set of the target team is then expanded combining the nodes given by users and their neighbor nodes with consideration of their preference and structure similarities. With the candidate node set as the core, the interests and preferences of a design team are mined to calculate the improved modularity and update the optimal team division. The experimental results on public datasets and crowdsourced engineering instance datasets show that the modularity index of team division is improved, which verifies the feasibility and practicability of the algorithm proposed in this paper.

Key words: crowdsourcing design, user preference, similarity, modularity

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