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数据分析与知识发现  2018, Vol. 2 Issue (5): 23-31     https://doi.org/10.11925/infotech.2096-3467.2017.1218
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
众包社区中基于敏感性分析的用户偏好挖掘模型及实验*
张亭亭1(), 赵宇翔2, 朱庆华3
1 南京大学工程管理学院 南京 210093
2 南京理工大学经济管理学院 南京 210094
3南京大学信息管理学院 南京 210093
Mining User Preferences in Crowdsourcing Community with Sensitivity Analysis
Zhang Tingting1(), Zhao Yuxiang2, Zhu Qinghua3
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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摘要 

【目的】 对众包社区中用户及任务特征进行分析, 识别出众包用户的潜在兴趣偏好。【方法】在现有研究的基础上, 本文运用敏感性分析方法研究了众包用户对各任务属性特征的敏感性程度, 并结合二部图原理构建相应的众包用户潜在偏好挖掘模型, 挖掘出众包用户行为规律中所包含的隐性偏好信息, 并通过实验分析说明了该模型的有效性。【结果】本文提出的模型可以有效识别众包用户对于Books、Software、Music等属性特征的敏感性程度, 并挖掘出用户对于Pyrex Oblong Roaster、Oxford、Cashback等任务的潜在偏好, 预测其选择倾向。较传统协同过滤算法相比, 具有更小的MAE值。【局限】 本文偏好挖掘模型仅从竞赛型众包环境中的用户角度出发, 尚未考虑到协作型众包中不同用户的兴趣特征间的互补。【结论】本文模型不仅能够全面准确理解众包用户兴趣偏好, 还能挖掘众包用户潜在的偏好信息, 使得众包任务的分配更具有针对性, 从而增加众包任务分配的准确性。

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张亭亭
赵宇翔
朱庆华
关键词 众包任务-用户匹配属性特征敏感性分析潜在偏好挖掘    
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
收稿日期: 2017-12-05      出版日期: 2018-06-20
ZTFLH:  N99  
基金资助:*本文系国家社会科学基金重大项目“面向大数据的数字图书馆移动视觉搜索机制及应用研究”(项目编号: 15ZDB126)、国家自然科学基金面上项目“基于科研众包模式的公众科学项目运作与管理机制研究”(项目编号: 71774083)和国家自然科学基金青年项目“基于行动者网络框架的众包模式设计与管理研究”(项目编号: 71403119)的研究成果之一
引用本文:   
张亭亭, 赵宇翔, 朱庆华. 众包社区中基于敏感性分析的用户偏好挖掘模型及实验*[J]. 数据分析与知识发现, 2018, 2(5): 23-31.
Zhang Tingting,Zhao Yuxiang,Zhu Qinghua. Mining User Preferences in Crowdsourcing Community with Sensitivity Analysis. Data Analysis and Knowledge Discovery, 2018, 2(5): 23-31.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1218      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I5/23
  用户和任务属性特征图
User ID Products Categories of the Products Rating
5247778 ‘Wader Basics Tow Truck’ ‘Family’ 5
5247778 ‘Arthur in a pickle - Marc Brown' 'Books' 4
5647565 'Straightheads (DVD)' 'DVDs' 4
3338 'Oxford (England)' 'Travel' 4
6496990 'Top Gun (Blu-ray)' 'DVDs' 5
  实验数据结构
用户 用户编号
众包用户1 5247778
众包用户2 5647565
众包用户3 3338
众包用户4 5769137
众包用户5 6496990
众包用户6 5241292
  社区众包用户表
众包任务 描述
任务1 Pyrex Oblong Roaster
任务2 Wader Basics Tow Truck
任务3 Straw Craft: More GoldenDollies-M.Lambeth
任务4 Arthur in a pickle-Marc Brown
任务5 Straight heads (DVD)
任务6 The Daughter of Time-Josephine Tey
任务7 Rebecca Wheatley-The New Me Workout(DVD)
任务8 Oxford (England)
任务9 Cashback (DVD)
任务10 Movie Collector: DVD Database Software
任务11 Free All Angels [ECD]-Ash
任务12 Take A Break''s Fate & Fortune
  社区众包任务表
属性 描述 属性 描述
属性1 House & Garden 属性6 Software
属性2 Family 属性7 Food & Drink
属性3 Books 属性8 Beauty
属性4 DVDs 属性9 Music
属性5 Travel 属性10 Entertainment
  属性特征表
  社区性众包用户对于任务属性的敏感性值
  社区用户对众包任务的预测偏好值
  初始社区众包用户对众包任务的评分值
  两种方法预测效果的比较
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