Publication Type
Journal Article
Version
acceptedVersion
Publication Date
11-2016
Abstract
Advanced networking technologies and massive online social media have stimulated a booming growth of travel heterogeneous information in recent years. By employing such information, smart travel guide systems, such as landmark ranking systems, have been proposed to offer diverse online travel services. It is essential for a landmark ranking system to structure, analyze, and search the travel heterogeneous information to produce human-expected results. Therefore, currently the most fundamental yet challenging problems can be concluded: 1) how to fuse heterogeneous tourism information and 2) how to model landmark ranking. In this paper, a novel landmark search system is introduced based on a newly designed heterogeneous information fusion scheme and a query-dependent landmark ranking strategy. Different from the existing travel guide systems, the proposed system can effectively combine the heterogeneous information from multimodality media into a landmark reranking list via a user's query. Experimental results conducted on a large travel information collection illustrate the advantages of the proposed system in terms of both effectiveness and efficiency.
Keywords
Heterogeneous multimedia analysis, information fusion, landmark reranking, travel guide
Discipline
Computer Sciences | Databases and Information Systems | Transportation
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Systems Man and Cybernetics: Systems
Volume
46
Issue
11
First Page
1492
Last Page
1504
ISSN
2168-2216
Identifier
10.1109/TSMC.2016.2523948
Publisher
IEEE
Citation
SHEN, Junge; SHEN, Jialie; MEI, Tao; and GAO, Xinbo.
Landmark reranking for smart travel guide systems by combining and analyzing diverse media. (2016). IEEE Transactions on Systems Man and Cybernetics: Systems. 46, (11), 1492-1504.
Available at: https://ink.library.smu.edu.sg/sis_research/3314
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TSMC.2016.2523948