Recommending Fair Payments for Large-Scale Social Ridesharing. Bistaffa, F., Farinelli, A., Chalkiadakis, G., & Ramchurn, S. D. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 139–146, 2015. ![Recommending Fair Payments for Large-Scale Social Ridesharing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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).
@inproceedings{
11562_927023,
author = {Bistaffa, Filippo and Farinelli, Alessandro and Chalkiadakis, Georgios and Ramchurn, Sarvapali D.},
title = {Recommending Fair Payments for Large-Scale Social Ridesharing},
year = {2015},
booktitle = {Proceedings of the 9th ACM Conference on Recommender Systems},
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).},
keywords = {Algorithm Scalability, Innovative Applications, Coalition Formation, Social Networks, Ridesharing},
url = {http://dl.acm.org/citation.cfm?id=2800177},
doi = {10.1145/2792838.2800177},
isbn = {978-145033692-5},
pages = {139--146}
}
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