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Community-Based Bayesian Aggregation Models for Crowdsourcing

Venanzi, Matteo, John, Guiver, Gabriella, Kazai, Pushmeet, Kohli and Milad, Shokouhi (2014) Community-Based Bayesian Aggregation Models for Crowdsourcing At the 23rd International World Wide Web Conference (WWW 2014). , pp. 155-164. (doi:10.1145/2566486.2567989).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper addresses the problem of extracting accurate labels from crowdsourced datasets, a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of individual workers, for instance, by way of confusion matrices, and using these latent traits to estimate the true labels more accurately. However, this strategy becomes ineffective when there are too few labels per worker to reliably estimate their quality. To mitigate this issue, we propose a novel community-based Bayesian label aggregation model, CommunityBCC, which assumes that crowd workers conform to a few different types, where each type represents a group of workers with similar confusion matrices. We assume that each worker belongs to a certain community, where the worker’s confusion matrix is similar to (a perturbation of) the community’s confusion matrix. Our model can then learn a set of key latent features: (i) the confusion matrix of each community, (ii) the community membership of each user, and (iii) the aggregated label of each item. We compare the performance of our model against established aggregation methods on a number of large-scale, real-world crowdsourcing datasets. Our experimental results show that our CommunityBCC model consistently outperforms state-of-the-art label aggregation methods, gaining, on average, 8% more accuracy with the same amount of labels.

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Published date: May 2014
Venue - Dates: the 23rd International World Wide Web Conference (WWW 2014), 2014-05-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 362614
URI: http://eprints.soton.ac.uk/id/eprint/362614
PURE UUID: a379a2ce-ac9e-4b16-9a31-851ee73e4a27

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Date deposited: 27 Feb 2014 15:47
Last modified: 18 Jul 2017 02:50

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Contributors

Author: Matteo Venanzi
Author: Guiver John
Author: Kazai Gabriella
Author: Kohli Pushmeet
Author: Shokouhi Milad

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