Towards task recommendation in micro-task markets

Vamshi Ambati, Stephan Vogel, Jaime Carbonell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

Abstract

As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests. We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.

Original languageEnglish
Title of host publicationAAAI Workshop - Technical Report
Pages80-83
Number of pages4
VolumeWS-11-11
Publication statusPublished - 2 Nov 2011
Externally publishedYes
Event2011 AAAI Workshop - San Francisco, CA, United States
Duration: 8 Aug 20118 Aug 2011

Other

Other2011 AAAI Workshop
CountryUnited States
CitySan Francisco, CA
Period8/8/118/8/11

Fingerprint

Recommender systems
Personnel
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ambati, V., Vogel, S., & Carbonell, J. (2011). Towards task recommendation in micro-task markets. In AAAI Workshop - Technical Report (Vol. WS-11-11, pp. 80-83)

Towards task recommendation in micro-task markets. / Ambati, Vamshi; Vogel, Stephan; Carbonell, Jaime.

AAAI Workshop - Technical Report. Vol. WS-11-11 2011. p. 80-83.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ambati, V, Vogel, S & Carbonell, J 2011, Towards task recommendation in micro-task markets. in AAAI Workshop - Technical Report. vol. WS-11-11, pp. 80-83, 2011 AAAI Workshop, San Francisco, CA, United States, 8/8/11.
Ambati V, Vogel S, Carbonell J. Towards task recommendation in micro-task markets. In AAAI Workshop - Technical Report. Vol. WS-11-11. 2011. p. 80-83
Ambati, Vamshi ; Vogel, Stephan ; Carbonell, Jaime. / Towards task recommendation in micro-task markets. AAAI Workshop - Technical Report. Vol. WS-11-11 2011. pp. 80-83
@inproceedings{c2f082ffb6424683b55e08e6d6cb1b4c,
title = "Towards task recommendation in micro-task markets",
abstract = "As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests. We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.",
author = "Vamshi Ambati and Stephan Vogel and Jaime Carbonell",
year = "2011",
month = "11",
day = "2",
language = "English",
isbn = "9781577355274",
volume = "WS-11-11",
pages = "80--83",
booktitle = "AAAI Workshop - Technical Report",

}

TY - GEN

T1 - Towards task recommendation in micro-task markets

AU - Ambati, Vamshi

AU - Vogel, Stephan

AU - Carbonell, Jaime

PY - 2011/11/2

Y1 - 2011/11/2

N2 - As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests. We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.

AB - As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests. We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.

UR - http://www.scopus.com/inward/record.url?scp=80055049926&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80055049926&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781577355274

VL - WS-11-11

SP - 80

EP - 83

BT - AAAI Workshop - Technical Report

ER -