New Approximation Guarantees for Minimum-Weight k-Trees and Prize-Collecting Salesmen
SIAM Journal on Computing
Future Generation Computer Systems
Improved algorithms for orienteering and related problems
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 18th international conference on World wide web
Deducing trip related information from flickr
Proceedings of the 18th international conference on World wide web
Mining tourist information from user-supplied collections
Proceedings of the 18th ACM conference on Information and knowledge management
City exploration by use of spatio-temporal analysis and clustering of user contributed photos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Identifying points of interest by self-tuning clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Using social media to find places of interest: a case study
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Detecting Places of Interest Using Social Media
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining tourist routes using Flickr traces
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Discovering and Characterizing Places of Interest Using Flickr and Twitter
International Journal on Semantic Web & Information Systems
| Hi-index | 0.00 |
We study how to automatically extract tourist trips from large volumes of geo-tagged photographs. Working with more than 8 million of these photographs that are publicly available via photo- sharing communities such as Flickr and Panoramio, our goal is to satisfy the needs of a tourist who specifies a starting location (typically a hotel) together with a bounded travel distance and demands a tour that visits the popular sites along the way. Our system, named ANTOURAGE, solves this intractable problem using a novel adaptation of the max-min ant system (MMAS) meta-heuristic. Experiments using GPS metadata crawled from Flickr show that ANTOURAGE can generate high-quality tours.