RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User's QoE

RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User's QoE

Nowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users' quality is a tangible n...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 20; no. 12; pp. 4728 - 4740
Main Authors: Lasmar, Eduardo L., de Paula, Fabio O., Rosa, Renata L., Abrahao, Julia I., Rodriguez, Demostenes Z.
Format: Journal Article
Language:English
Published: New York IEEE 01-12-2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Car pools
Machine learning
Machine learning algorithms
mobile applications
online social network services
Public transportation
Quality of experience
Real-time systems
recommendation systems
Recommender systems
Ride sharing services
ridesharing
Smart phones
Social networking (online)
Social networks
Transportation
Urban transportation
User satisfaction
Vehicles
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Summary:Nowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users' quality is a tangible novel evaluation parameter. Consequently, this study treats the use of quality of experience (QoE) in the ridesharing service context, proposing a recommendation system (RS) for ridesharing services (RsRS), which considers user profile information extracted from online social networks (OSN) and user preferences. Thus, the main objective of the proposed RsRS is to improve users' QoE. To this end, the users' profile for the ridesharing service is built based on OSN data, which includes group of users with similar characteristics in the same trip, thus avoiding users with opposite preferences. First, subjective tests are carried out to obtain information on users' preferences and the results are analyzed via machine learning algorithms to determine the various user profiles. The experimental results demonstrate that the random forest algorithm has the best performance, considering OSN data and explicit preferences saved in the proposed solution and only OSN data, for average F-measures of 0.92 and 0.91, respectively. Additionally, a ranking containing a list of recommended users to share a ride is determined using a similarity function, and the results demonstrate that 94.2% of assessors agree with the proposed recommendations. Furthermore, the RsRS has a modular configuration and its integration with a real ridesharing service providers is also discussed.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2945793