中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
Title:
Benchmarking big data for trip recommendation
Author: Liu, Kuien (1) ; Li, Yaguang (1) ; Ding, Zhiming (1) ; Shang, Shuo (2) ; Zheng, Kai (3)
Conference Name: 2014 23rd International Conference on Computer Communication and Networks, ICCCN 2014
Conference Date: August 4, 2014 - August 7, 2014
Issued Date: 2014
Conference Place: Shanghai, China
Corresponding Author: Liu, Kuien
Publish Place: Institute of Electrical and Electronics Engineers Inc.
Indexed Type: EI
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
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16626
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
Liu, Kuien ,Li, Yaguang ,Ding, Zhiming ,et al. Benchmarking big data for trip recommendation[C]. 见:2014 23rd International Conference on Computer Communication and Networks, ICCCN 2014. Shanghai, China. August 4, 2014 - August 7, 2014.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Liu, Kuien (1)]'s Articles
[Li, Yaguang (1)]'s Articles
[Ding, Zhiming (1)]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Liu, Kuien (1)]‘s Articles
[Li, Yaguang (1)]‘s Articles
[Ding, Zhiming (1)]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace