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A New Approach for Learning User Preferences for a Ridesharing Application

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Transactions on Computational Collective Intelligence XXVIII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 10780))

Abstract

Ridesharing has the potential to relieve some transportational issues such as traffic congestion, pollution and high travel costs. In this paper, we focus on the process of matching drivers and prospective riders more effectively, which is a crucial challenge in ridesharing. A novel approach is proposed in ride-matching which involves learning user preferences regarding the desirability of a choice of matching; this could then maintain high user satisfaction, thus encouraging repeat usage of the system. An SVM inspired method is developed which is able to learn a scoring function from a set of pairwise comparisons, and predicts the satisfaction degree of the user with respect to specific matches. To assess the proposed approach, we conducted some experiments on a commercial ridesharing data set. We compare the proposed approach with five rival strategies and methods, and the results clearly show the merits of our approach.

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Notes

  1. 1.

    https://www.gocarma.com/.

  2. 2.

    The original formula is \(\text {DCG}_p=rel_1+\sum _{i=2}^{p}\frac{rel_i}{\log _2(i)}\). It can be seen that the coefficient of the first term (\(rel_1\)) and the second term’s (\(rel_2\)) are equal. To give a smaller penalty value to the first term, we divided \(rel_1\) by 0.4 (instead of 1).

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Acknowledgment

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, and also supported by Carma (http://carmacarpool.com). We are grateful for helpful discussions with Vincent Armant, Cristina Cornelio, Martin Gerner, Barry O’Sullivan, Conor Roche and Gilles Simonin.

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Authors and Affiliations

  1. School of Computer Science and IT, Insight Centre for Data Analytics, University College Cork, Cork, Ireland

    Mojtaba Montazery & Nic Wilson

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  1. Mojtaba Montazery
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  2. Nic Wilson
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Correspondence to Mojtaba Montazery .

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Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Victoria, Australia

    Ryszard Kowalczyk

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  4. Universidade do Porto, Porto, Portugal

    Ana Paula Rocha

  5. INSTICC, Polytechnic Institute of Setúbal, Setubal, Portugal

    Joaquim Filipe

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Montazery, M., Wilson, N. (2018). A New Approach for Learning User Preferences for a Ridesharing Application. In: Nguyen, N., Kowalczyk, R., van den Herik, J., Rocha, A., Filipe, J. (eds) Transactions on Computational Collective Intelligence XXVIII. Lecture Notes in Computer Science(), vol 10780. Springer, Cham. https://doi.org/10.1007/978-3-319-78301-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-78301-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78300-0

  • Online ISBN: 978-3-319-78301-7

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