A crowd-based route recommendation system-CrowdPlanner

Su, Han, Zheng, Kai, Huang, Jiamin, Liu, Tianyu, Wang, Haozhou and Zhou, Xiaofang (2014). A crowd-based route recommendation system-CrowdPlanner. In: 2014 IEEE 30th International Conference on Data Engineering, ICDE 2014. 30th IEEE International Conference on Data Engineering, ICDE 2014, Chicago, IL United States, (1178-1181). 31 March-4 April 2014. doi:10.1109/ICDE.2014.6816735

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Author Su, Han
Zheng, Kai
Huang, Jiamin
Liu, Tianyu
Wang, Haozhou
Zhou, Xiaofang
Title of paper A crowd-based route recommendation system-CrowdPlanner
Conference name 30th IEEE International Conference on Data Engineering, ICDE 2014
Conference location Chicago, IL United States
Conference dates 31 March-4 April 2014
Convener IEEE
Proceedings title 2014 IEEE 30th International Conference on Data Engineering, ICDE 2014   Check publisher's open access policy
Journal name 2014 Ieee 30Th International Conference On Data Engineering (Icde)   Check publisher's open access policy
Place of Publication Washington, DC United States
Publisher IEEE Computer Society
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/ICDE.2014.6816735
Open Access Status
ISBN 9781479925544
ISSN 1084-4627
Start page 1178
End page 1181
Total pages 4
Collection year 2015
Language eng
Abstract/Summary Route recommendation service has become a big business in industry since traveling is now an important part of our daily life. We can travel to unknown places by simply typing in our destination and then following recommendation service's guidance, that a pleasant trip desires them to provide a good route. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers since the many latent factors affect drivers' preferences and it is hard for a single route recommendation algorithm to model all of them. In this demo we will present the CrowPlanner system to leverage crowds' knowledge to improve the recommendation quality. It requests human workers to evaluate candidates routes recommended by different sources and methods, and determines the best route based on the feedbacks of these workers. In this demo, we first introduce the core component of our system for smart question generation, and then show several real route recommendation cases and the feedback of users.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

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