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Recommending fair payments for large-scale social ridesharing

Recommending fair payments for large-scale social ridesharing
Recommending fair payments for large-scale social ridesharing
We perform recommendations for the Social Ridesharing scenario, in which a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on how much one should pay for taking a ride with friends. More formally, we propose the first approach that can compute fair coalitional payments that are also stable according to the game-theoretic concept of the kernel for systems with thousands of agents in real-world scenarios. Our tests, based on real datasets for both spatial (GeoLife) and social data (Twitter), show that our approach is significantly faster than the state-of-the-art (up to 84 times), allowing us to compute stable payments for 2000 agents in 50 minutes. We also develop a parallel version of our approach, which achieves a near-optimal speed-up in the number of processors used. Finally, our empirical analysis reveals new insights into the relationship between payments incurred by a user by virtue of its position in its social network and its role (rider or driver).
139-146
Bistaffa, Filippo
c3867bb6-ac44-472e-bb89-e5ed315cdedd
Farinelli, Alessandro
1d096018-a929-4ff4-9b2a-308458863213
Chalkiadakis, Georgios
50ef5d10-3ffe-4253-ac88-fad4004240e7
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Bistaffa, Filippo
c3867bb6-ac44-472e-bb89-e5ed315cdedd
Farinelli, Alessandro
1d096018-a929-4ff4-9b2a-308458863213
Chalkiadakis, Georgios
50ef5d10-3ffe-4253-ac88-fad4004240e7
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Bistaffa, Filippo, Farinelli, Alessandro, Chalkiadakis, Georgios and Ramchurn, Sarvapali (2015) Recommending fair payments for large-scale social ridesharing. RecSys'15, Austria. 16 - 20 Sep 2015. pp. 139-146 . (doi:10.1145/2792838.2800177).

Record type: Conference or Workshop Item (Paper)

Abstract

We perform recommendations for the Social Ridesharing scenario, in which a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on how much one should pay for taking a ride with friends. More formally, we propose the first approach that can compute fair coalitional payments that are also stable according to the game-theoretic concept of the kernel for systems with thousands of agents in real-world scenarios. Our tests, based on real datasets for both spatial (GeoLife) and social data (Twitter), show that our approach is significantly faster than the state-of-the-art (up to 84 times), allowing us to compute stable payments for 2000 agents in 50 minutes. We also develop a parallel version of our approach, which achieves a near-optimal speed-up in the number of processors used. Finally, our empirical analysis reveals new insights into the relationship between payments incurred by a user by virtue of its position in its social network and its role (rider or driver).

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More information

Accepted/In Press date: 19 June 2015
Published date: September 2015
Venue - Dates: RecSys'15, Austria, 2015-09-16 - 2015-09-20
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 379241
URI: https://eprints.soton.ac.uk/id/eprint/379241
PURE UUID: e8d142a3-9005-4abc-b1c5-921aae2d19b9
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 21 Jul 2015 13:59
Last modified: 20 Jul 2019 00:56

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