Benchmarking big data for trip recommendation

ISCAS OpenIR

	Benchmarking big data for trip recommendation
	Liu, Kuien (1); Li, Yaguang (1); Ding, Zhiming (1); Shang, Shuo (2); Zheng, Kai (3); Liu, Kuien
	2014
会议名称	2014 23rd International Conference on Computer Communication and Networks, ICCCN 2014
会议日期	August 4, 2014 - August 7, 2014
会议地点	Shanghai, China
收录类别	EI
出版地	Institute of Electrical and Electronics Engineers Inc.
ISSN	10952055
ISBN	9781479935727
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Department of Software Engineering, China University of Petroleum, Beijing, China; (3) School of Information Technology and Electrical Engineering, University of Queensland, Brisbane; QLD, Australia
摘要	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.; 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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16626
专题	中国科学院软件研究所
通讯作者	Liu, Kuien
推荐引用方式 GB/T 7714	Liu, Kuien ,Li, Yaguang ,Ding, Zhiming ,et al. Benchmarking big data for trip recommendation[C]. Institute of Electrical and Electronics Engineers Inc.,2014.