Benchmarking Big Data for Trip Recommendation

Liu, Kuien, Li, Yaguang, Ding, Zhiming, Shang, Shuo and Zheng, Kai (2014). Benchmarking Big Data for Trip Recommendation. In: 2014 23rd International Conference On Computer Communication and Networks (ICCCN). 23rd International Conference on Computer Communication and Networks (ICCCN), Shanghai, Peoples Republic of China, (). 4-7 August 2014. doi:10.1109/ICCCN.2014.6911842


Author Liu, Kuien
Li, Yaguang
Ding, Zhiming
Shang, Shuo
Zheng, Kai
Title of paper Benchmarking Big Data for Trip Recommendation
Conference name 23rd International Conference on Computer Communication and Networks (ICCCN)
Conference location Shanghai, Peoples Republic of China
Conference dates 4-7 August 2014
Proceedings title 2014 23rd International Conference On Computer Communication and Networks (ICCCN)
Publisher IEEE
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/ICCCN.2014.6911842
Open Access Status Not yet assessed
ISBN 9781479935727
ISSN 1095-2055
Total pages 6
Language eng
Abstract/Summary The availability of massive trajectory data collected from GPS devices has received significant attentions in recent years. A hot topic is trip recommendation, which focuses on searching trajectories that connect (or are close to) a set of query locations, e.g., several sightseeing places specified by a traveller, from a collection of historic trajectories made by other travellers. However, if we know little about the sample coverage of trajectory data when developing an application of trip recommendation, it is difficult for us to answer many practical questions, such as 1) how many (future) queries can be supported with a given set of raw trajectories? 2) how many trajectories are required to achieve a good-enough result? 3) how frequent the update operations need to be performed on trajectory data to keep it long-term effective? In this paper, we focus on studying the overall quality of trajectory data from both spatial and temporal domains and evaluate proposed methods with a real big trajectory dataset. Our results should be useful for both the development of trip recommendation systems and the improvement of trajectory-searching algorithms.
Keyword Big Data
Trip Recommendation
Spatio-temporal Trajectory Data
Benchmark
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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