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Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity

Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity
Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity
We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the re- porting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application.
717-724
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440
Teacy, W.T.L.
5f962a10-9ab5-4b19-8016-cc72588bdc6a
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440
Teacy, W.T.L.
5f962a10-9ab5-4b19-8016-cc72588bdc6a
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Venanzi, Matteo, Teacy, W.T.L., Rogers, Alex and Jennings, Nicholas R. (2015) Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity. International Joint Conference on Artificial Intelligence (IJCAI-15), Argentina. 25 - 31 Jul 2015. pp. 717-724 .

Record type: Conference or Workshop Item (Paper)

Abstract

We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the re- porting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application.

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More information

Published date: July 2015
Venue - Dates: International Joint Conference on Artificial Intelligence (IJCAI-15), Argentina, 2015-07-25 - 2015-07-31
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 376365
URI: https://eprints.soton.ac.uk/id/eprint/376365
PURE UUID: 9d751785-842c-4c53-9c4c-26a3b6256958

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Date deposited: 18 Apr 2015 09:17
Last modified: 07 Oct 2019 18:28

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