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Crowdsourcing

一、             技术简介

Crowdsourcing, a modern business term coined in 2005, is defined by Merriam-Webster as the process of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, especially an online community, rather than from employees or suppliers. A portmanteau of crowd and outsourcing, its more specific definitions are yet heavily debated…

资料来源：https://en.wikipedia.org/wiki/Crowdsourcing

二、             相关领域

Computer Science

Statistics

Outsourcing

三、             研究趋势

四、             技术分析

在2012年之后，涌现大量新的研究。

最近几个月，研究的热度呈增长趋势。

维基百科上相关的内容更新较为迅速。

五、             相关学者

排名	姓名	所属机构	发文数
1	Jiawei Han	University of Illinois	42
2	Michael S Bernstein	Stanford University	2
3	Robert C Miller	Massachusetts Institute of Technology	2
4	Jeffrey P Bigham	Carnegie Mellon University	2
5	Bjorn Hartmann	University of California Berkeley	1

六、             相关机构

排名	名称	发文数
1	University of California Berkeley	2
2	Stanford University	2
3	University of Michigan	2
4	Bard College	2
5	Columbia University	1
6	Carnegie Mellon University	1
7	Massachusetts Institute of Technology	1
8	University of Pennsylvania	1
9	Microsoft	1
10	Google	1

七、             相关文献

1/10

【篇名】 Crowdsourcing: Leveraging Innovation through Online Idea Competitions

【作者】 Schweitzer, Fiona Maria; Buchinger, Walter; Gassmann, Oliver; Obrist, Marianna

【出处】 Research-Technology Management

【详细】 Volume 55, Number 3, May-June 2012, pp. 32-38(7)

【摘要】 Along with other Web 2.0 market intelligence tools, online idea competitions can provide essential input for decision making in the early phases of product innovation. However, in order to use online competitions effectively, it is essential to know when to use which method, how to use it, and to what extent virtual and conventional research techniques can be used interchangeably or complementarily. A first step toward assessing the power of Web 2.0 techniques is to compare them with traditional ones. We compare the expense and results of online idea competitions with focus groups for idea generation in the market for senior citizen mobile phones and services. We find that idea competitions lead to more and better ideas at a lower cost per idea, while focus groups yield richer interactions with users.

2/10

【篇名】 Intelligent Control of Crowdsourcing

【作者】 Daniel S. Weld        University of Washington, Seattle, WA, USA

【出处】IUI '15 Proceedings of the 20th International Conference on Intelligent User Interfaces

【详细】 Pages 1-1

【摘要】 Crowd-sourcing labor markets (e.g., Amazon Mechanical Turk) are booming, because they enable rapid construction of complex workflows that seamlessly mix human computation with computer automation. Example applications range from photo tagging to audio-visual transcription and interlingual translation. Similarly, workflows on citizen science sites (e.g. GalaxyZoo) have allowed ordinary people to pool their effort and make interesting discoveries. Unfortunately, constructing a good workflow is difficult, be- cause the quality of the work performed by humans is highly variable. Typically, a task designer will experiment with several alternative workflows to accomplish a task, varying the amount of redundant labor, until she devises a control strategy that delivers acceptable performance. Fortunately, this control challenge can often be formulated as an automated planning problem ripe for algorithms from the probabilistic planning and reinforcement learning literature. I describe our recent work on the decision-theoretic control of crowd sourcing and suggest open problems for future research.

3/10

【篇名】 Adaptive and Interoperable Crowdsourcing

【作者】 Marco Brambilla ; Stefano Ceri ; Andrea Mauri ; Riccardo Volonterio

【出处】 IEEE Internet Computing

【详细】 Volume:19 , Issue: 5, Page: 36 - 44

【摘要】 Crowd-based computing is an increasingly popular paradigm for building Web applications, which uses the collective strength of human actors for performing tasks that are more suited to humans than computers. Interaction with the crowds was originally confined to specifically designed crowdsourcing platforms, such as Amazon Mechanical Turk. More recently, crowd-based computing has been reconsidered and extended, targeting social networks such as Facebook, Twitter, or LinkedIn, or including basic and direct interaction mechanisms, such as routing personal emails or tweets. Crowdsearcher, the system presented here, fosters interoperability and adaptation in crowd-based applications -- for example, the ability of supporting multiplatform applications and adapting them in reaction to events. This approach specifically supports dynamic interoperability (that is, the ability to modify the execution platforms while the application is ongoing) as a reaction to crowd behavior, which is hardly predictable. The authors show how to specify interoperability control at a high, declarative level and then implement it using active rules, thereby obtaining answers from crowds engaged in different communities. They also show the approach's effect on precision, delay, and cost.