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Crowdsourcing systems on the World-Wide Web

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TLDR
The practice of crowdsourcing is transforming the Web and giving rise to a new field of inquiry called "crowdsourcing", which aims to provide real-time information about events in a democratic manner.
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This article is published in Communications of The ACM.The article was published on 2011-04-01. It has received 1165 citations till now. The article focuses on the topics: Crowdsourcing & Web navigation.

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Business intelligence and analytics: from big data to big impact

TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
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Towards an integrated crowdsourcing definition

TL;DR: In this article, existing definitions of crowdsourcing are analysed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative.
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CrowdDB: answering queries with crowdsourcing

TL;DR: The design of CrowdDB is described, a major change is that the traditional closed-world assumption for query processing does not hold for human input, and important avenues for future work in the development of crowdsourced query processing systems are outlined.
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Evaluation on crowdsourcing research: Current status and future direction

TL;DR: A critical examination of the substrate of crowdsourcing research is presented by surveying the landscape of existing studies, including theoretical foundations, research methods, and research foci, and identifies several important research directions for IS scholars from three perspectives—the participant, organization, and system—and which warrant further study.
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CrowdER: crowdsourcing entity resolution

TL;DR: This work proposes a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are use to verify only the most likely matching pairs, and develops a novel two-tiered heuristic approach for creating batched tasks.
References
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Proceedings ArticleDOI

Item-based collaborative filtering recommendation algorithms

TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
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A survey of approaches to automatic schema matching

TL;DR: A taxonomy is presented that distinguishes between schema-level and instance-level, element- level and structure- level, and language-based and constraint-based matchers and is intended to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.
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Labeling images with a computer game

TL;DR: A new interactive system: a game that is fun and can be used to create valuable output that addresses the image-labeling problem and encourages people to do the work by taking advantage of their desire to be entertained.
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Mining knowledge-sharing sites for viral marketing

TL;DR: This research optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him, and takes into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost.
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reCAPTCHA: Human-Based Character Recognition via Web Security Measures

TL;DR: This research explored whether human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize.