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.
ISSN10952055
ISBN9781479935727
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.
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