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| Benchmarking big data for trip recommendation | |
| Liu, Kuien (1); Li, Yaguang (1); Ding, Zhiming (1); Shang, Shuo (2); Zheng, Kai (3); Liu, Kuien | |
| 2014 | |
| Conference Name | 2014 23rd International Conference on Computer Communication and Networks, ICCCN 2014 |
| Conference Date | August 4, 2014 - August 7, 2014 |
| Conference Place | Shanghai, China |
| Indexed Type | EI |
| Publish Place | Institute of Electrical and Electronics Engineers Inc. |
| ISSN | 10952055 |
| ISBN | 9781479935727 |
| Department | (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 |
| English Abstract | 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. |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16626 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Liu, Kuien |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
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