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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 23-31    DOI: 10.11925/infotech.2096-3467.2017.1218
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Mining User Preferences in Crowdsourcing Community with Sensitivity Analysis
Tingting Zhang1(),Yuxiang Zhao2,Qinghua Zhu3
1School of Engineering and Management, Nanjing University, Nanjing 210093, China
2School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
3School of Information Management, Nanjing University, Nanjing 210093, China
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Abstract  

[Objective] This paper analyzes the attributes and task characteristics of crowdsourcing community users, aiming to identify their potential interests or preferences. [Methods] First, we studied the user’s sensitivity to task attributes based on sensitivity analysis method, and constructed a model for mining user’s potential preferences with bipartite graph. Then, we used this model to discover the implicit preferences from user’s behaviors. Finally, we confirmed the validity of the proposed model through experimental analysis. [Results] Our model could effectively identify the degrees of users’ sensitivity to Books, Software, and Music etc. It could also discover users’ potential interests or preferences to Pyrex Oblong Roaster, Oxford, and Cashback etc. to predict their choices. Compared with traditional collaborative filtering algorithms, the proposed model has a smaller MAE value. [Limitations] Our preferences mining model is based on users in the competitive environment, and it does not consider the complementarity among the interests of collaborative users. [Conclusions] The proposed model could accurately understand the users’ interests in crowdsourcing community, and then reveal their potential preferences. It helps us effectively distribute crowdsourcing tasks.

Key wordsCrowdsourcing      Task-User Fit      Attribute Feature      Sensitivity Analysis      Potential Preferences Mining     
Received: 05 December 2017      Published: 20 June 2018

Cite this article:

Tingting Zhang,Yuxiang Zhao,Qinghua Zhu. Mining User Preferences in Crowdsourcing Community with Sensitivity Analysis. Data Analysis and Knowledge Discovery, 2018, 2(5): 23-31.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1218     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I5/23

[1] Howe J.The Rise of Crowdsourcing[J]. Wired Magazine, 2006, 14(6): 1-4.
[2] Zhao Y, Zhu Q.Evaluation on Crowdsourcing Research: Current Status and Future Direction[J]. Information Systems Frontiers, 2014, 16(3): 417-434.
[3] Nakatsu R T, Grossman E B, Iacovou C L.A Taxonomy of Crowdsourcing Based on Task Complexity[J]. Journal of Information Science, 2014, 40(6): 823-834.
[4] Zhao Y, Zhu Q.Effects of Extrinsic and Intrinsic Motivation on Participation in Crowdsourcing Contest[J]. Online Information Review, 2014, 38(7): 896-917.
[5] 李肇明. 基于个人兴趣的用户偏好建模[D]. 昆明: 云南大学, 2013.
[5] (Li Zhaoming.User Preference Modeling Based on Personal Interest[D]. Kunming: Yunnan University, 2013.)
[6] Zhao Y, Zhu Q.Conceptualizing Task Affordance in Online Crowdsourcing Context[J]. Online Information Review, 2016, 40(7): 938-958.
[7] Ho C J, Jabbari S, Vaughan J W.Adaptive Task Assignment for Crowdsourced Classification[C]//Proceedings of International Conference on Machine Learning. 2013: 534-542.
[8] Feldman M, Bernstein A.Cognition-based Task Routing: Towards Highly-Effective Task-Assignments in Crowdsourcing Settings[C]//Proceedings of the 35th International Conference on Information Systems(ICIS), Auckland, New Zealand.2014.
[9] Yuen M C, King I, Leung K S.Task Matching in Crowdsourcing[C]//Proceedings of the IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing. 2012: 409-412.
[10] Geiger D, Schader M.Personalized Task Recommendation in Crowdsourcing Information Systems — Current State of the Art[J]. Decision Support Systems, 2014, 65(C): 3-16.
[11] Herlocker J L, Konstan J A, Terveen L G, et al.Evaluating Collaborative Filtering Recommender Systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
[12] 胡昌平, 邵其赶, 孙高岭. 个性化信息服务中的用户偏好与行为分析[J]. 情报理论与实践, 2008, 31(1): 4-6.
[12] (Hu Changping, Shao Qigan, Sun Gaoling.User Preference and Behavior Analysis in Individual Information Service[J]. Information Studies: Theory & Application, 2008, 31(1): 4-6.)
[13] 刘远超, 王晓龙, 刘秉权, 等. 基于聚类分析策略的用户偏好挖掘[J]. 计算机应用研究, 2005, 22(12): 21-23.
[13] (Liu Yuanchao, Wang Xiaolong, Liu Bingquan, et al.A Cluster-based Approach on Mining Text Preference[J]. Application Research of Computers, 2005, 22(12): 21-23.)
[14] 孔繁超. 个性化信息服务中用户偏好的动态挖掘[J]. 情报理论与实践, 2009, 32(6): 111-113.
[14] (Kong Fanchao.Dynamic Mining of User Preferences in Individual Information Service[J]. Information Studies: Theory & Application, 2009, 32(6): 111-113.)
[15] 林霜梅, 汪更生, 陈弈秋. 个性化推荐系统中的用户建模及特征选择[J]. 计算机工程, 2007, 33(17): 196-198.
[15] (Lin Shuangmei, Wang Gengsheng, Chen Yiqiu.User Modeling and Feature Selection in Personalized Recommending System[J]. Computer Engineering, 2007, 33(17): 196-198.)
[16] 朱小宁, 双锴, 程祥. 基于用户兴趣和能力实现任务分发的众包平台[OL]. 中国科技论文在线, 2014. .
[16] (Zhu Xiaoning, Shuang Kai, Cheng Xiang. A Crowdsourcing Platform for Task Distribution Based on User Interest and Expertise[OL]. China Sciencepaper Online, 2014.
[17] Minder P, Seuken S, Bernstein A.Crowdmanager- combinatorial Allocation and Pricing of Crowdsourcing Tasks with Time Constraints[C]// Proceedings of the Workshop on Social Computing and User Generated Content in Conjunction with ACM Conference on Electronic Commerce, Valencia, Spain.2012: 1-18.
[18] Karger D R, Oh S, Shah D.Budget-optimal Crowdsourcing Using Low-rank Matrix Approximations[C]// Proceedings of the 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). 2011.
[19] Peter J P, Olson J C.Consumer Behavior & Marketing Strategy[M]. McGraw-Hill, 2010.
[20] 贾大文, 曾承, 彭智勇, 等. 一种基于用户偏好自动分类的社会媒体共享和推荐方法[J]. 计算机学报, 2012, 35(11): 2381-2391.
[20] (Jia Dawen, Zeng Cheng, Peng Zhiyong, et al.A User Preference Based Automatic Potential Group Generation Method for Social Media Sharing and Recommendation[J]. Chinese Journal of Computers, 2012, 35(11): 2381-2391.)
[21] 李聪, 梁昌勇. 基于属性值偏好矩阵的协同过滤推荐算法[J]. 情报学报, 2008, 27(6): 884-890.
[21] (Li Cong, Liang Changyong.A Collaborative Filtering Recommendation Algorithm Based on Attributes-value Preference Matrix[J]. Journal of the China Society for Scientific and Technical Information, 2008, 27(6): 884-890.)
[22] Horvath T.A Model of User Preference Learning for Content-Based Recommender Systems[J]. Computing & Informatics, 2012, 28(4): 453-481.
[23] Frey H C, Patil S R.Identification and Review of Sensitivity Analysis Methods[J]. Risk Analysis, 2002, 22(3): 553-578.
[24] Boudreau K J, Lakhani K R.Using the Crowd as an Innovation Partner[J]. Harvard Business Review, 2013, 91(4): 60-69.
[25] Afuah A, Tucci C L.Crowdsourcing as a Solution to Distant Search[J]. Academy of Management Review, 2012, 37(3): 355-375.
[26] Kleinbaum D, Kupper L.Applied Regression Analysis and Other Multivariate Methods[J]. Technometrics, 2008, 31(1): 117-118.
[27] 何晓群, 刘文卿. 应用回归分析[M]. 北京: 中国人民大学出版社, 2007: 58-79.
[27] (He Xiaoqun, Liu Wenqing.Applied Regression Analysis[M]. Beijing: China Renmin University Press, 2007: 58-79.)
[28] 张新猛, 蒋盛益. 基于加权二部图的个性化推荐算法[J]. 计算机应用, 2012, 32(3): 654-657, 678.
[28] (Zhang Xinmeng, Jiang Shengyi.Personalized Recommendation Algorithm Based on Weighted Bipartite Network[J]. Journal of Computer Applications, 2012, 32(3): 654-657, 678.)
[29] Zhou T, Ren J, Medo M, et al.How to Project a Bipartite Network?[J]. Physics, 2007, 76(4): 70-80.
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