面向众包平台的偏好和结构相似度融合式设计团队发现

doi:10.3969/j.issn.1007-7375.2023.04.002

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

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. 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 Dianting^1,2, WU Shan¹, ZHAO Sijia¹, SHANG Lei¹, YE Hengzhou²

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/Abstract

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

中图分类号:

TH-9
TP301.6

刘电霆, 吴珊, 赵思佳, 尚磊, 叶恒舟. 面向众包平台的偏好和结构相似度融合式设计团队发现[J]. 工业工程, 2023, 26(4): 9-15.

LIU Dianting, WU Shan, ZHAO Sijia, SHANG Lei, YE Hengzhou. A Method of Designer Team Discovery Integrating Preference and Structure Similarities for Crowdsourcing Platforms[J]. Industrial Engineering Journal, 2023, 26(4): 9-15.

参考文献

[1] HOW E J. The rise of crowdsourcing[J]. Wired Magazine, 2006, 14 (6): 176-183.
[2] 夏恩君, 赵轩维, 李森. 国外众包研究现状和趋势[J]. 技术经济, 2015, 34(1): 28-36
XIA Enjun, ZHAO Xuanwei, LI Sen. Crowdsourcing research: statue and prospect[J]. Technical Economy, 2015, 34(1): 28-36
[3] 王震, 欧阳啸, 郭伟. 众包设计平台工作者关系网络构建与分析[J]. 计算机集成制造系统, 2022, 28(8): 2522-2533.
WANG Zhen, OUYANG Xiao, GUO Wei. Construction and analysis of worker’s relationship network of crowdsourcing design platform[J]. Computer Integrated Manufacturing Systems, 2022, 28(8): 2522-2533.
[4] 潘仁志, 钱付兰, 赵姝, 等. 基于卷积神经网络交互的用户属性偏好建模的推荐模型[J]. 计算机应用, 2022, 42(2): 404-411
PAN Renzhi, QIAN Fulan, ZHAO Shu, et al. Recommendation model for user attribute preference modeling based on convolutional neural network interaction[J]. Journal of Computer Applications, 2022, 42(2): 404-411
[5] 王晓燕, 李劲华. 用户偏好模型在众包中应用的研究[J]. 青岛大学学报 (自然科学版), 2018, 31(1): 102-108
WANG Xiaoyan, LI Jinhua. The Research of the Applying User Interest Model to Crowdsourcing[J]. Journal of Qingdao University (Natural Science Edition), 2018, 31(1): 102-108
[6] 武聪, 马文明, 王冰, 等. 融合用户标签相似度的矩阵分解算法[J]. 南京大学学报 (自然科学), 2022, 58(1): 143-152
WU Cong, MA Wenming, WANG Bing, et al. Matrix factorization algorithm combined with user tag similarity[J]. Journal of Nanjing University (Natural Science), 2022, 58(1): 143-152
[7] HORVATH T. A model of user preference learning for content-based recommender systems[J]. Computing & Informatics, 2012, 28(4): 453-481
[8] LIN C. Signals in the silence: models of implicit feedback in a recommendation system for crowdsourcing[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Quebec City, Canada: AAAI Press, 2014: 908-915.
[9] GUNNEMANN S, FARBER I, RAUBACH S, et al. Spectral subspace clustering for graphs with feature vectors[C]//Proceedings of 14th International Conference on Data Mining. NY: IEEE Computer Society, 2014, 231-240.
[10] 姜东明, 杨火根. 融合图卷积网络模型的无监督社区检测算法[J]. 计算机工程与应用, 2020, 56(20): 59-66
JIANG Dongming, YANG Huogen. Unsupervised community detection algorithm integrating graph convolutional network model[J]. Computer Engineering and Applications, 2020, 56(20): 59-66
[11] WU P, PAN L. Mining target attribute subspace and set of target communities in large attributed networks[EB/OL]. (2017-05-10). http://arXiv.org/abs/1705.03590vl, 2017.
[12] 孟彩霞, 李楠楠, 张琰. 基于复杂网络的社区发现算法研究[J]. 计算机技术与发展, 2020, 30(1): 82-86
MENG Caixia, LI Nannan, ZHANG Yan. Research on community detection algorithm based on complex network[J]. Computer Technology and Development, 2020, 30(1): 82-86
[13] 王静红, 于雅智. 复杂网络半监督的社区发现算法研究[J]. 计算机应用研究, 2018, 35(6): 1663-1667
WANG Jinghong, YU Yazhi. Research algorithm of semi-supervised general community detection on complex networks[J]. Application Research of Computers, 2018, 35(6): 1663-1667
[14] 杨晓光, 朱保平. 基于复杂网络的社区发现算法[J]. 南京理工大学学报, 2016, 40(3): 267-271
YANG Xiaoguang, ZHU Baoping. Community detection algorithm based on complex network[J]. Journal of Nanjing University of Science and Technology, 2016, 40(3): 267-271
[15] 王亚珅, 黄河燕, 冯冲. 基于最小割图分割的社区发现算法[J]. 中文信息学报, 2017, 31(3): 213-222
WANG Yashen, HUANG Heyan, FENG Chong. Community detection based on minimum-cut graph partitioning[J]. Journal of Chinese Information Processing, 2017, 31(3): 213-222
[16] 褚叶祺, 丁佳骏. 基于Louvain算法的作者合著网络社区划分研究[J]. 高技术通讯, 2021, 31(3): 257-262
CHU Yeqi, DING Jiajun. Research on community detetion in co-authorship networks based on Louvain algorithm[J]. Chinese High Technology Letters, 2021, 31(3): 257-262
[17] 吴祖峰, 王鹏飞, 秦志光, 等. 改进的Louvain社团划分算法[J]. 电子科技大学学报, 2013, 42(1): 105-108
WU Zufeng, WANG Pengfei, QIN Zhiguang, et al. Improved algorithm of Louvain communities dipartition[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(1): 105-108
[18] NEWMAN M E J. Spectral methods for community detection and graph partitioning[J]. Physical Review E-Statistical, Nonlinear and Soft Matter Physics, 2013, 88(4-1): 042822
[19] 夏玮, 杨鹤标. 改进的Louvain算法及其在推荐领域的研究[J]. 信息技术, 2017(11): 125-128
XIA Wei, YANG Hebiao. Optimization of Louvain algorithm and its application in personalized recommendation[J]. Information Technology, 2017(11): 125-128
[20] 王海燕, 孙成成. 一种基于多视图学习的群组发现方法[J]. 南京邮电大学学报 (自然科学版), 2019, 39(4): 80-87
WANG Haiyan, SUN Chengcheng. Group discovery method based on multi-view learning[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2019, 39(4): 80-87

面向众包平台的偏好和结构相似度融合式设计团队发现

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

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