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Crowd Computing: Opportunities & Challenges (IJCNLP 2011 Keynote)

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Keynote on Crowd Computing presented at The 5th International Joint Conference on Natural Language Processing (IJCNLP2011) on November 10th in Chiang Mai, Thailand

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Crowd Computing: Opportunities & Challenges (IJCNLP 2011 Keynote)

  1. 1. Crowd Computing:Opportunities and Challenges Matt Lease School of Information University of Texas at Austin ml@ischool.utexas.edu @mattlease
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  3. 3. Amazon Mechanical Turk (MTurk)• Marketplace for crowd labor (microtasks)• Created in 2005 (still in “beta”)• On-demand, scalable, 24/7 global workforce 3
  4. 4. 4
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  6. 6. Labeling Data (“Gold Rush”) 6
  7. 7. Snow et al. (EMNLP 2008)• MTurk annotation for 5 Tasks – Affect recognition – Word similarity – Recognizing textual entailment – Event temporal ordering – Word sense disambiguation• 22K labels for US $26• High agreement between consensus labels and gold-standard labels 7
  8. 8. Alonso et al. (SIGIR Forum 2008)• MTurk for Information Retrieval (IR) – Judge relevance of search engine results• Many follow-on studies (design, quality, cost) 8
  9. 9. Sorokin & Forsythe (CVPR 2008)• MTurk for Computer Vision• 4K labels for US $60 9
  10. 10. Studying People 10
  11. 11. Kittur, Chi, & Suh (CHI 2008)• MTurk for User Studies• “…make creating believable invalid responses as effortful as completing the task in good faith.” 11
  12. 12. Jane saw the man with the binoculars 12
  13. 13. Social & Behavioral Sciences• A Guide to Behavioral Experiments on Mechanical Turk – W. Mason and S. Suri (2010). SSRN online.• Crowdsourcing for Human Subjects Research – L. Schmidt (CrowdConf 2010)• Crowdsourcing Content Analysis for Behavioral Research: Insights from Mechanical Turk – Conley & Tosti-Kharas (2010). Academy of Management• Amazons Mechanical Turk : A New Source of Inexpensive, Yet High-Quality, Data? – M. Buhrmester et al. (2011). Perspectives… 6(1):3-5. – see also: Amazon Mechanical Turk Guide for Social Scientists 13
  14. 14. Studying Interactive Systems 14
  15. 15. Remote Usability Testing• D. Liu, R. Kuipers, M. Lease, and R. Bias (in preparation)• On-site vs. crowdsourced usability testing• Advantages – More Participants – More Diverse Participants – High Speed – Low Cost• Disadvantages – Lower Quality Feedback – Less Interaction – Greater need for quality control – Less Focused User Groups 15
  16. 16. Beyond MTurk 16
  17. 17. ESP Game (Games With a Purpose)L. Von Ahn and L. Dabbish (2004) 17
  18. 18. 18
  19. 19. reCaptchaL. von Ahn et al. (2008). In Science. 19
  20. 20. Human Sensing and Monitoring• Sullivan et al. (2009). Bio. Conservation (142):10• Keynote by Steve Kelling at ASIS&T 2011 20
  21. 21. 21
  22. 22. Human Computation 22
  23. 23. The Mechanical TurkThe original, constructed andunveiled in 1770 by Wolfgangvon Kempelen (1734–1804) J. Pontin. Artificial Intelligence, With Help From the Humans. New York Times (March 25, 2007) 23
  24. 24. The Turing Test (Alan Turing, 1950) 24
  25. 25. 25
  26. 26. The Turing Test (Alan Turing, 1950) 26
  27. 27. What is a Computer? 27
  28. 28. • What was old is new• Crowdsourcing: A New Branch of Computer Science – D.A. Grier, March 29, 2011• Tabulating the heavens: computing the Nautical Almanac in 18th-century England – M. Croarken (2003) Princeton University Press, 2005 28
  29. 29. The Human Processing Unit (HPU)• Davis et al. (2010) HPU 29
  30. 30. Human Computation• Luis von Ahn (2005)• Investigates use of people to execute certain computations for which capabilities of current automated methods are more limited• Explores the metaphor of computation for characterizing attributes, capabilities, and limitations of human performance in executing desired tasks• Having people do stuff (instead of computers) 30
  31. 31. Blending Automation & Human Computation 31
  32. 32. “Amazon Remembers” 32
  33. 33. Ethics Checking: The Next Frontier?• Mark Johnson’s address at ACL 2003 – Transcript in Conduit 12(2) 2003• Think how useful a little “ethics checker and corrector” program integrated into a word processor could be! 33
  34. 34. Soylent: A Word Processor with a Crowd Inside • Bernstein et al., UIST 2010 34
  35. 35. Translation by monolingual speakers• C. Hu, CHI 2009 35
  36. 36. fold.itS. Cooper et al. (2010)Alice G. Walton. Online Gamers Help Solve Mystery ofCritical AIDS Virus Enzyme. The Atlantic, October 8, 2011. 36
  37. 37. CrowdSearch and mCrowd• T. Yan, MobiSys 2010 37
  38. 38. VizWiz aaaaaaaaBingham et al. (UIST 2010) 38
  39. 39. 39
  40. 40. Crowdsourcing• Jeff Howe. Wired, June 2006.• Take a job traditionally performed by a known agent (often an employee)• Outsource it to an undefined, generally large group of people via an open call• New application of principles from open source movement 40
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  42. 42. What is Crowdsourcing?• A collection of mechanisms and associated methodologies for scaling and directing crowd activities to achieve some goal(s)• Enabled by internet-connectivity• Many related areas – Human computation – Collective intelligence – Social computing – People services 42
  43. 43. Crowdsourcing Key Questions• What are the goals? – Purposeful directing of human activity• How can you incentivize participation? – Incentive engineering – Who is your intended crowd?• Which model(s) are most appropriate? – How to adapt them to your context and goals? 43
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  45. 45. What about sensitive data?• Not all data can be publicly disclosed – User data (e.g. AOL query log, Netflix ratings) – Intellectual property – Legal confidentiality• Need to restrict who is in your crowd – Separate channel (workforce) from technology – Hot question for adoption at enterprise level 45
  46. 46. What about the law?• Wolfson & Lease (ASIS&T 2011)• As usual, technology is ahead of the law – employment law – patent inventorship – data security and the Federal Trade Commission – copyright ownership – securities regulation of crowdfunding• Take-away: don’t panic, but be mindful – Understand risks of “just in-time compliance” 46
  47. 47. Nature of Micro-tasks• Small, simple tasks which can be completed without extraneous detail or context – e.g. “Can you name who is in this photo?”• Variety of current research investigating how to decompose complex tasks into simpler ones 47
  48. 48. Context Matters 48
  49. 49. What about trust?• Some reports of robot “workers” on MTurk – E.g. McCreadie et al. (2011) – Violates terms of service• Why not just use a captcha? 49
  50. 50. Captcha Fraud 50
  51. 51. Requester Fraud on MTurk“Do not do any HITs that involve: filling inCAPTCHAs; secret shopping; test our web page;test zip code; free trial; click my link; surveys orquizzes (unless the requester is listed with asmiley in the Hall of Fame/Shame); anythingthat involves sending a text message; orbasically anything that asks for any personalinformation at all—even your zip code. If youfeel in your gut it’s not on the level, IT’S NOT.Why? Because they are scams...” 51
  52. 52. Who arethe workers?• A. Baio, November 2008. The Faces of Mechanical Turk.• P. Ipeirotis. March 2010. The New Demographics of Mechanical Turk• J. Ross, et al. Who are the Crowdworkers?... CHI 2010. 52
  53. 53. What about ethics?• Silberman, Irani, and Ross (2010) – “How should we… conceptualize the role of these people who we ask to power our computing?” – Power dynamics between parties • What are the consequences for a worker when your actions harm their reputation? – “Abstraction hides detail”• Fort, Adda, and Cohen (2011) – “…opportunities for our community to deliberately value ethics above cost savings.” 53
  54. 54. Davis et al. (2010) The HPU. HPU 54
  55. 55. HPU: “Abstraction hides detail”• Not just turning a mechanical crank 55
  56. 56. 56
  57. 57. What about quality? 57
  58. 58. What about quality?• Many papers on statistical methods for MTurk – Worker calibration, noise vs. bias, weighted voting – Checking consensus may discourage worker honesty• Garbage in = garbage out – Only as strong as your weakest link (end-to-end) – Is your shiny statistical hammer what’s really needed? – Not all problems are technological• Methods for consistent annotation still apply 58
  59. 59. What about quality? (2)• Human factors matter – Part of your experimental design (study & report) – Instructions, design, interface, interaction – Names, relationship, reputation (Klinger & Lease 2011) – Fair pay, hourly vs. per-task, recognition, advancement – For contrast, consider Kochhar (2010)• How good is gold really? – Klebanov and Beigman (NAACL 2010) – Model uncertainty in ground truth • Training and evaluation with uncertain labels • Temporal and label uncertainty in active learning 59
  60. 60. Need for Benchmarks• How well do different crowdsourcing methods perform on comparable data? – Shared datasets used to bring people together• Common tasks to consider – Translation – Transcription – Relevance Judging (search engines) – Generation (resources, SEO, reputation) – Verification (correct, copyright, appropriate)• NIST TREC Crowdsourcing Track exploring this – Track will run for 2nd Year in 2012 60
  61. 61. The Road Ahead 61
  62. 62. Why Eytan Adar hates MTurk Research (CHI 2011 CHC Workshop)• Overly-narrow focus on MTurk – Identify general vs. platform-specific problems – Academic vs. Industrial problems• Inattention to prior work in other disciplines – Some problems well-studied in other areas – Human behavior hasn’t changed much• Turks aren’t Martians – How many prior user studies need to be reproduced on MTurk before we believe it? 62
  63. 63. Many choices beyond MTurk• Clickworker Industry heavy-weights• CloudCrowd Elance• CloudFactory Liveops• CrowdSource oDesk• DoMyStuff uTest• Humanoid• Microtask And More! JobBoy, microWorkers, MiniFreelance,• MobileWorks MiniJobz, MinuteWorkers, MyEasyTask, OpTask, ShortTask, SimpleWorkers• myGengo• SmartSheet• vWorker 63
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  65. 65. Run-time automation for managing distributed crowd computing• Kittur et al. (CHI 2011), CrowdForge 65
  66. 66. MapReduce for human computation?• Large task divided into smaller sub-problems• Job distributed among multiple workers• Collect all answers and combine them• Varying performance of heterogeneous CPUs/HPUs 66
  67. 67. Wisdom of Crowds ComputingPre-conditions• Diversity• Independence• Decentralization• AggregationInput: large, diverse sample (increases likelihood of overall pool quality)Output: consensus, selection, distributionWhat about ensemble techniques and theorywe have for integrating multiple noisy models? Computational Social Science & the Wisdom of Crowds NIPS 2010-2011 workshops 67
  68. 68. • How to effectively compute in the crowd? – What are all the knobs and dials? – How to set them to navigate quality vs. cost vs. time?• How do we design, implement, test, & maintain crowd computing systems?• What new capabilities can such systems provide?• How does cheaper / faster / easier / noisier data change the way we build intelligent systems? – Relative costs of all other activities increase 68
  69. 69. Unreasonable Effectiveness of Data• Massive free Web data changed how we train learning systems – Banko and Brill (2001). Human Language Tech. – Halevy et al. (2009). IEEE Intelligent Systems. • How might access to cheap & plentiful labeled data change the balance again? 69
  70. 70. • Who is the right person for the job? – Requesting or inferring skills / experience – Interactive task selection or automatic routing – How to represent, measure, model, estimate, and utilize individual worker expertise/accuracy?• How should crowd computing evolve from here? – What should next generation infrastructure provide? – What are the right programming constructs?• With automation and human computation, who does what? Mixed-initiative thinking. 70
  71. 71. Crowdsourcing in 2012• Conferences and Workshops – AAAI Symposium: Wisdom of the Crowd (March 26-28) – Collective Intelligence (papers: Nov 18, date: April 18-20) – Year 2 of TREC Crowdsourcing Track – HComp and CrowdConf (details TBD)• Journal Special Issues – Springer’s Information Retrieval: Crowdsourcing for Information Retrieval – Hindawi’s Advances in Multimedia Journal: Multimedia Semantics Analysis via Crowdsourcing Geocontext – IEEE Internet Computing: Crowdsourcing (Sept./Oct. 2012) – IEEE Transactions on Multimedia: Crowdsourcing in Multimedia (proposal in review)• Places for News – Follow the Crowd – The Crowdsortium 71
  72. 72. 2011 Tutorials and Keynotes• By Omar Alonso and/or Matthew Lease – CLEF: Crowdsourcing for Information Retrieval Experimentation and Evaluation (Sep. 20, Omar only) – CrowdConf (Nov. 1, this is it!) – IJCNLP: Crowd Computing: Opportunities and Challenges (Nov. 10, Matt only) – WSDM: Crowdsourcing 101: Putting the WSDM of Crowds to Work for You (Feb. 9) – SIGIR: Crowdsourcing for Information Retrieval: Principles, Methods, and Applications (July 24)• AAAI: Human Computation: Core Research Questions and State of the Art – Edith Law and Luis von Ahn, August 7• ASIS&T: How to Identify Ducks In Flight: A Crowdsourcing Approach to Biodiversity Research and Conservation – Steve Kelling, October 10, ebird• EC: Conducting Behavioral Research Using Amazons Mechanical Turk – Winter Mason and Siddharth Suri, June 5• HCIC: Quality Crowdsourcing for Human Computer Interaction Research – Ed Chi, June 14-18, about HCIC) – Also see his: Crowdsourcing for HCI Research with Amazon Mechanical Turk• Multimedia: Frontiers in Multimedia Search – Alan Hanjalic and Martha Larson, Nov 28• VLDB: Crowdsourcing Applications and Platforms – Anhai Doan, Michael Franklin, Donald Kossmann, and Tim Kraska)• WWW: Managing Crowdsourced Human Computation – Panos Ipeirotis and Praveen Paritosh 72
  73. 73. 2011 Workshops & Conferences• AAAI-HCOMP: 3rd Human Computation Workshop (Aug. 8)• ACIS: Crowdsourcing, Value Co-Creation, & Digital Economy Innovation (Nov. 30 – Dec. 2)• Crowdsourcing Technologies for Language and Cognition Studies (July 27)• CHI-CHC: Crowdsourcing and Human Computation (May 8)• CIKM: BooksOnline (Oct. 24, “crowdsourcing … online books”)• CrowdConf 2011 -- 2nd Conf. on the Future of Distributed Work (Nov. 1-2)• Crowdsourcing: Improving … Scientific Data Through Social Networking (June 13)• EC: Workshop on Social Computing and User Generated Content (June 5)• ICWE: 2nd International Workshop on Enterprise Crowdsourcing (June 20)• Interspeech: Crowdsourcing for speech processing (August)• NIPS: Second Workshop on Computational Social Science and the Wisdom of Crowds (Dec. TBD)• SIGIR-CIR: Workshop on Crowdsourcing for Information Retrieval (July 28)• TREC-Crowd: Year 1 of TREC Crowdsourcing Track (Nov. 16-18)• UbiComp: 2nd Workshop on Ubiquitous Crowdsourcing (Sep. 18)• WSDM-CSDM: Crowdsourcing for Search and Data Mining (Feb. 9) 73
  74. 74. Recent Overview Papers• Alex Quinn and Ben Bederson. Human Computation: A Survey and Taxonomy of a Growing Field. CHI 2011.• Man-Ching Yuen, Irwin King, and Kwong-Sak Leung. A Survey of Crowdsourcing Systems. SocialCom 2011.• Rajarshi Das and Maja Vukovic. Emerging theories and models of human computation systems: a brief survey. UbiCrowd 2011• A. Doan, R. Ramakrishnan, A. Halevy. Crowdsourcing Systems on the World-Wide Web. Communications of the ACM, 2011. 74
  75. 75. Books• Omar Alonso, Gabriella Kazai, and Stefano Mizzaro. (2012). Crowdsourcing for Search Engine Evaluation: Why and How.• Law and von Ahn (2011). Human Computation 75
  76. 76. More BooksJuly 2010, kindle-only: “This book introduces you to thetop crowdsourcing sites and outlines step by step withphotos the exact process to get started as a requester onAmazon Mechanical Turk.“ 76
  77. 77. Thank You! ir.ischool.utexas.edu/crowd• Students – Catherine Grady (iSchool) Matt Lease – Hyunjoon Jung (ECE) ml@ischool.utexas.edu – Jorn Klinger (Linguistics) @mattlease – Adriana Kovashka (CS) – Abhimanu Kumar (CS) – Di Liu (iSchool) – Hohyon Ryu (iSchool) – William Tang (CS) – Stephen Wolfson (iSchool)• Omar Alonso, Microsoft Bing• Support – John P. Commons 77
  78. 78. Bibliography J. Barr and L. Cabrera. “AI gets a Brain”, ACM Queue, May 2006. Bernstein, M. et al. Soylent: A Word Processor with a Crowd Inside. UIST 2010. Best Student Paper award. Bederson, B.B., Hu, C., & Resnik, P. Translation by Interactive Collaboration between Monolingual Users, Proceedings of Graphics Interface (GI 2010), 39-46. N. Bradburn, S. Sudman, and B. Wansink. Asking Questions: The Definitive Guide to Questionnaire Design, Jossey-Bass, 2004. C. Callison-Burch. “Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk”, EMNLP 2009. P. Dai, Mausam, and D. Weld. “Decision-Theoretic of Crowd-Sourced Workflows”, AAAI, 2010. J. Davis et al. “The HPU”, IEEE Computer Vision and Pattern Recognition Workshop on Advancing Computer Vision with Human in the Loop (ACVHL), June 2010. M. Gashler, C. Giraud-Carrier, T. Martinez. Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous, ICMLA 2008. D. A. Grier. When Computers Were Human. Princeton University Press, 2005. ISBN 0691091579 JS. Hacker and L. von Ahn. “Matchin: Eliciting User Preferences with an Online Game”, CHI 2009. J. Heer, M. Bobstock. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design”, CHI 2010. P. Heymann and H. Garcia-Molina. “Human Processing”, Technical Report, Stanford Info Lab, 2010. J. Howe. “Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business”. Crown Business, New York, 2008. P. Hsueh, P. Melville, V. Sindhwami. “Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria”. NAACL HLT Workshop on Active Learning and NLP, 2009. B. Huberman, D. Romero, and F. Wu. “Crowdsourcing, attention and productivity”. Journal of Information Science, 2009. P.G. Ipeirotis. The New Demographics of Mechanical Turk. March 9, 2010. PDF and Spreadsheet. P.G. Ipeirotis, R. Chandrasekar and P. Bennett. Report on the human computation workshop. SIGKDD Explorations v11 no 2 pp. 80-83, 2010. P.G. Ipeirotis. Analyzing the Amazon Mechanical Turk Marketplace. CeDER-10-04 (Sept. 11, 2010) 78
  79. 79. Bibliography (2) A. Kittur, E. Chi, and B. Suh. “Crowdsourcing user studies with Mechanical Turk”, SIGCHI 2008. Aniket Kittur, Boris Smus, Robert E. Kraut. CrowdForge: Crowdsourcing Complex Work. CHI 2011 Adriana Kovashka and Matthew Lease. “Human and Machine Detection of … Similarity in Art”. CrowdConf 2010. K. Krippendorff. "Content Analysis", Sage Publications, 2003 G. Little, L. Chilton, M. Goldman, and R. Miller. “TurKit: Tools for Iterative Tasks on Mechanical Turk”, HCOMP 2009. T. Malone, R. Laubacher, and C. Dellarocas. Harnessing Crowds: Mapping the Genome of Collective Intelligence. 2009. W. Mason and D. Watts. “Financial Incentives and the ’Performance of Crowds’”, HCOMP Workshop at KDD 2009. J. Nielsen. “Usability Engineering”, Morgan-Kaufman, 1994. A. Quinn and B. Bederson. “A Taxonomy of Distributed Human Computation”, Technical Report HCIL-2009-23, 2009 J. Ross, L. Irani, M. Six Silberman, A. Zaldivar, and B. Tomlinson. “Who are the Crowdworkers?: Shifting Demographics in Amazon Mechanical Turk”. CHI 2010. F. Scheuren. “What is a Survey” (http://www.whatisasurvey.info) 2004. R. Snow, B. O’Connor, D. Jurafsky, and A. Y. Ng. “Cheap and Fast But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks”. EMNLP-2008. V. Sheng, F. Provost, P. Ipeirotis. “Get Another Label? Improving Data Quality … Using Multiple, Noisy Labelers” KDD 2008. S. Weber. “The Success of Open Source”, Harvard University Press, 2004. L. von Ahn. Games with a purpose. Computer, 39 (6), 92–94, 2006. L. von Ahn and L. Dabbish. “Designing Games with a purpose”. CACM, Vol. 51, No. 8, 2008. 79
  80. 80. Bibliography (3) Shuo Chen et al. What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers. AAAI 2010. Paul Heymann, Hector Garcia-Molina: Turkalytics: analytics for human computation. WWW 2011. Florian Laws, Christian Scheible and Hinrich Schütze. Active Learning with Amazon Mechanical Turk. EMNLP 2011. C.Y. Lin. Rouge: A package for automatic evaluation of summaries. Proceedings of the workshop on text summarization branches out (WAS), 2004. C. Marshall and F. Shipman “The Ownership and Reuse of Visual Media”, JCDL, 2011. Hohyon Ryu and Matthew Lease. Crowdworker Filtering with Support Vector Machine. ASIS&T 2011. Wei Tang and Matthew Lease. Semi-Supervised Consensus Labeling for Crowdsourcing. ACM SIGIR Workshop on Crowdsourcing for Information Retrieval (CIR), 2011. S. Vijayanarasimhan and K. Grauman. Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds. CVPR 2011. Stephen Wolfson and Matthew Lease. Look Before You Leap: Legal Pitfalls of Crowdsourcing. ASIS&T 2011. 80
  81. 81. Crowdsourcing in IR: 2008-2010 2008  O. Alonso, D. Rose, and B. Stewart. “Crowdsourcing for relevance evaluation”, SIGIR Forum, Vol. 42, No. 2. 2009  O. Alonso and S. Mizzaro. “Can we get rid of TREC Assessors? Using Mechanical Turk for … Assessment”. SIGIR Workshop on the Future of IR Evaluation.  P.N. Bennett, D.M. Chickering, A. Mityagin. Learning Consensus Opinion: Mining Data from a Labeling Game. WWW.  G. Kazai, N. Milic-Frayling, and J. Costello. “Towards Methods for the Collective Gathering and Quality Control of Relevance Assessments”, SIGIR.  G. Kazai and N. Milic-Frayling. “… Quality of Relevance Assessments Collected through Crowdsourcing”. SIGIR Workshop on the Future of IR Evaluation.  Law et al. “SearchWar”. HCOMP.  H. Ma, R. Chandrasekar, C. Quirk, and A. Gupta. “Improving Search Engines Using Human Computation Games”, CIKM 2009. 2010  SIGIR Workshop on Crowdsourcing for Search Evaluation.  O. Alonso, R. Schenkel, and M. Theobald. “Crowdsourcing Assessments for XML Ranked Retrieval”, ECIR.  K. Berberich, S. Bedathur, O. Alonso, G. Weikum “A Language Modeling Approach for Temporal Information Needs”, ECIR.  C. Grady and M. Lease. “Crowdsourcing Document Relevance Assessment with Mechanical Turk”. NAACL HLT Workshop on … Amazons Mechanical Turk.  Grace Hui Yang, Anton Mityagin, Krysta M. Svore, and Sergey Markov . “Collecting High Quality Overlapping Labels at Low Cost”. SIGIR.  G. Kazai. “An Exploration of the Influence that Task Parameters Have on the Performance of Crowds”. CrowdConf.  G. Kazai. “… Crowdsourcing in Building an Evaluation Platform for Searching Collections of Digitized Books”., Workshop on Very Large Digital Libraries (VLDL)  Stephanie Nowak and Stefan Ruger. How Reliable are Annotations via Crowdsourcing? MIR.  Jean-François Paiement, Dr. James G. Shanahan, and Remi Zajac. “Crowdsourcing Local Search Relevance”. CrowdConf.  Maria Stone and Omar Alonso. “A Comparison of On-Demand Workforce with Trained Judges for Web Search Relevance Evaluation”. CrowdConf.  T. Yan, V. Kumar, and D. Ganesan. CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. MobiSys pp. 77--90, 2010. 81
  82. 82. Crowdsourcing in IR: 2011 WSDM Workshop on Crowdsourcing for Search and Data Mining. SIGIR Workshop on Crowdsourcing for Information Retrieval. O. Alonso and R. Baeza-Yates. “Design and Implementation of Relevance Assessments using Crowdsourcing, ECIR 2011. Roi Blanco, Harry Halpin, Daniel Herzig, Peter Mika, Jeffrey Pound, Henry Thompson, Thanh D. Tran. “Repeatable and Reliable Search System Evaluation using Crowd-Sourcing”. SIGIR 2011. Yen-Ta Huang, An-Jung Cheng, Liang-Chi Hsieh, Winston H. Hsu, Kuo-Wei Chang. “Region-Based Landmark Discovery by Crowdsourcing Geo-Referenced Photos.” SIGIR 2011. Hyun Joon Jung, Matthew Lease . “Improving Consensus Accuracy via Z-score and Weighted Voting”. HCOMP 2011. G. Kasneci, J. Van Gael, D. Stern, and T. Graepel, CoBayes: Bayesian Knowledge Corroboration with Assessors of Unknown Areas of Expertise, WSDM 2011. Gabriella Kazai,. “In Search of Quality in Crowdsourcing for Search Engine Evaluation”, ECIR 2011. Gabriella Kazai, Jaap Kamps, Marijn Koolen, Natasa Milic-Frayling. “Crowdsourcing for Book Search Evaluation: Impact of Quality on Comparative System Ranking.” SIGIR 2011. Abhimanu Kumar, Matthew Lease . “Learning to Rank From a Noisy Crowd”. SIGIR 2011. Edith Law, Paul N. Bennett, and Eric Horvitz. “The Effects of Choice in Routing Relevance Judgments”. SIGIR 2011. 82