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2018 | OriginalPaper | Chapter

A New Approach for Learning User Preferences for a Ridesharing Application

Authors : Mojtaba Montazery, Nic Wilson

Published in: Transactions on Computational Collective Intelligence XXVIII

Publisher: Springer International Publishing

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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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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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Title: A New Approach for Learning User Preferences for a Ridesharing Application
Authors: Mojtaba Montazery
Nic Wilson
Publisher: Springer International Publishing
Book: Transactions on Computational Collective Intelligence XXVIII
Print ISBN: 978-3-319-78300-0

Electronic ISBN: 978-3-319-78301-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-78301-7_1

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