Cited By
View all- Zhang JJiang WZhang JWu JWang G(2021)Exploring Weather Data to Predict Activity Attendance in Event-based Social NetworkACM Transactions on the Web10.1145/344013415:2(1-25)Online publication date: 22-Apr-2021
Travel Recommendation System Using Geotagged Photos
Abstract
Recently in multimedia, web services contain a huge volume of geo-tagged photos. The users who upload these photos are sharing their travel experiences through them. Geo-tagged photos have crucial information imbibed within them, like location, time, tags and weather. Travel Recommendation methods that exist do not take into consideration user preferences and weather all at once. In this paper, a travel recommendation system is proposed for tourists in Mumbai according to their preferences, weather and live events. The preferences are obtained according to the prior travel history of user(s) and recommendations are suggested. Dataset is collected from the Flickr API and the technique is examined for Mumbai, an Indian metropolitan city. The effectiveness of the proposed method can be seen from the experimental results, which shows an average of 15% improvement in the accuracy with respect to the existing methods.
References
[1]
T. Kurashima, T. Iwata, G. Irie, and K. Fujimura, ---Travel route recommendation using geotags in photo sharing sites,∥ In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 2010, pp. 579--588.
[2]
A. Popescu, G. Grefenstette, ---Deducing trip related information from flickr,∥ In Proceedings of the 19th International Conference on World Wide Web, ACM, 2009, pp. 1183--1184.
[3]
H. Yin, X. Lu, C. Wang, N. Yu, and L. Zhang, ---Photo2trip: an interactive trip planning system based on geo-tagged photos,∥ In Proceedings of the International Conference on Multimedia, ACM, 2010, pp. 1579--1582.
[4]
X. Lu, C. Wang, J. M. Yang, Y. Pang, and L. Zhang, ---Photo2trip: generating travel routes from geo-tagged photos for trip planning,∥ In Proceedings of the International Conference on Multimedia, ACM, 2010, pp. 143--152.
[5]
Y. Arase, X. Xie, T. Hara, and S. Nishio, ---Mining people's trips from large scale geotagged photos,∥ In Proceedings of the ACM International Conference on Information and Multimedia, ACM, 2010, pp. 133--142.
[6]
T. Kurashima, T. Iwata, G. Irie, and K. Fujimura, ---Travel route recommendation using geotags in photo sharing sites,∥ In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 2010, pp. 579--588.
[7]
A. J. Cheng, Y. Y. Chen, Y. T. Huang, W. H. Hsu, and H. Y. M. Liao, ---Personalized travel recommendation by mining people attributes from community-contributed photos,? In Proceedings of the International Conference on Multimedia, ACM, 2011, pp. 83--92.
[8]
Y. Y. Chen, A. J. Cheng, and W. H. Hsu. ---Travel Recommendation by Mining People Attributes and Travel Group Types From Community Contributed Photos,∥ IEEE Transactions on multimedia, 2013, 15(6): pp. 1283--1295.
[9]
A. Majid, L. Chen, G. Chen, H. T. Mirza, and I. Hussain, ---GoThere: travel suggestions using geotagged photos,∥ In Proceedings of the 21st International Conference on World Wide Web., ACM, 2012, pp. 577--578.
[10]
A. Majid, L. Chen, G. Chen, H. T. Mirza, I. Hussain, J. Woodward, ---A contextaware personalized travel recommendation system based on geotagged social media data mining,∥ International Journal of Geographical Information Science, 2013, 27(4) pp. 662--684.
[11]
A. Majid, L. Chen, G. Chen, H. T. Mirza, I. Hussain, J. Woodward, ---A contextaware personalized travel recommendation system based on geotagged social media data mining,∥ International Journal of Geographical Information Science, 2013, 27(4) pp. 662--684.
[12]
Z. Yin, L. Cao, J. Han, J. Luo, and T. Huang, ---Diversified trajectory pattern ranking in geo-tagged social media,∥ In Proceedings of the SIAM International Conference on Data Mining, 2011, pp. 980--991
[13]
Y. Zheng, L. Zhang, X. Xie, and W. Y. Ma, ---Mining interesting locations and travel sequences from GPS trajectories,∥ In Proceedings of the 19th International Conference on World Wide Web, ACM, 2009, pp. 791--800. Hyatt, R.M. (1997). The Dynamic Tree-Splitting Parallel Search Algorithm. ICCA Journal, Vol. 20, No. 1, pp. 3--19.
[14]
Ping Hsieh, Cheng-Te L Hsun,∥ Traveling Path Recommendation Using Temporal Transit Patterns∥, Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan, Research Center for Information Technology, Academia Sinica, Taipei, Taiwan 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org)
[15]
Hegshu Zhu, Enghong Chen, University of Science and Technology of China, Hui Xiong, Rutgers University, Kuifei Yu and Huanhuan Cao, Nokia Research Center Jilei Tian, Nokia ---Mining Mobile User Preferences for Personalized Context-Aware Recommendation∥, 12th IEEE International Conference on Data Mining (ICDM'2012).
[16]
Martin Ester, Hans-Peter Kriegel, Jiirg Sander, Xiaowei Xu ---A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise∥, Institute for Computer Science, University of Munich, 1996, AAAI (www.aaai.org).
[17]
Online: https://www.wunderground.com/ Accessed on: March 2017
[18]
Online: https://www.flickr.com/ Accessed on: March 2017
[19]
Jian Pei, Jiawei Han Behzad, Mortazavi-Asl Helen Pinto, Qiming Chen, Umeshwar Dayal, Mei-Chun Hsu, ---PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth∥, Intelligent Database Systems Research Lab. School of Computing Science, Simon Fraser University Burnaby, B.C., Canada, Hewlett-Packard Labs. Palo Alto, California, Natural Sciences and Engineering Research Council of Canada (grant NSERC-A3723), the Networks of Centres of Excellence of Canada (grant NCE/IRIS-3), the Hewlett-Packard Lab, U.S.A.
[20]
D. Sculley ---Large Scale Learning to Rank∥, Google Inc.
Index Terms
- Travel Recommendation System Using Geotagged Photos
Recommendations
Personalized travel recommendation by mining people attributes from community-contributed photos
MM '11: Proceedings of the 19th ACM international conference on MultimediaLeveraging community-contributed data (e.g., blogs, GPS logs, and geo-tagged photos) for travel recommendation is one of the active researches since there are rich contexts and trip activities in such explosively growing data. In this work, we focus on ...
Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist
In recent years, million geo-tagged photos are available in online web service like Flickr, panoramio, etc. People contributing geo-tagged photo and share their travel experiences these media. The photo itself has important information sharing reveals ...
Travel route recommendation using geotagged photos
We propose a travel route recommendation method that makes use of the photographers' histories as held by social photo-sharing sites. Assuming that the collection of each photographer's geotagged photos is a sequence of visited locations, photo-sharing ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2017
157 pages
ISBN:9781450353243
DOI:10.1145/3154979
Copyright © 2017 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 November 2017
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCCT-2017
ICCCT-2017: International Conference on Computer and Communication Technology
November 24 - 26, 2017
Allahabad, India
Acceptance Rates
ICCCT-2017 Paper Acceptance Rate 33 of 124 submissions, 27%;
Overall Acceptance Rate 33 of 124 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 191Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)2
Reflects downloads up to 17 Nov 2024
Other Metrics
Citations
Cited By
View all- Zhang JJiang WZhang JWu JWang G(2021)Exploring Weather Data to Predict Activity Attendance in Event-based Social NetworkACM Transactions on the Web10.1145/344013415:2(1-25)Online publication date: 22-Apr-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in