Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr
With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great import...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/6/11/345 |
| _version_ | 1819036256309346304 |
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| author | Liu Yang Lun Wu Yu Liu Chaogui Kang |
| author_facet | Liu Yang Lun Wu Yu Liu Chaogui Kang |
| author_sort | Liu Yang |
| collection | DOAJ |
| description | With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns. |
| first_indexed | 2024-12-21T08:02:38Z |
| format | Article |
| id | doaj.art-cbc8926a9ee145aa9cc8f4ea3d17f5df |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T08:02:38Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-cbc8926a9ee145aa9cc8f4ea3d17f5df2022-12-21T19:10:52ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-11-0161134510.3390/ijgi6110345ijgi6110345Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from FlickrLiu Yang0Lun Wu1Yu Liu2Chaogui Kang3School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaWith millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.https://www.mdpi.com/2220-9964/6/11/345geo-tagged phototourist mobilitytravel motifpopular landmarkuser clustering |
| spellingShingle | Liu Yang Lun Wu Yu Liu Chaogui Kang Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr ISPRS International Journal of Geo-Information geo-tagged photo tourist mobility travel motif popular landmark user clustering |
| title | Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr |
| title_full | Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr |
| title_fullStr | Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr |
| title_full_unstemmed | Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr |
| title_short | Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr |
| title_sort | quantifying tourist behavior patterns by travel motifs and geo tagged photos from flickr |
| topic | geo-tagged photo tourist mobility travel motif popular landmark user clustering |
| url | https://www.mdpi.com/2220-9964/6/11/345 |
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