TY - GEN
T1 - Diversified trajectory pattern ranking in geo-tagged social media
AU - Yin, Zhijun
AU - Cao, Liangliang
AU - Han, Jiawei
AU - Luo, Jiebo
AU - Huang, Thomas
PY - 2011
Y1 - 2011
N2 - Social media such as those residing in the popular photo sharing websites is attracting increasing attention in recent years. As a type of user-generated data, wisdom of the crowd is embedded inside such social media. In particular, millions of users upload to Flickr their photos, many associated with temporal and geographical information. In this paper, we investigate how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps. The main objective is to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos. Instead of focusing on mining frequent trajectory patterns from geo-tagged social media, we put more effort into ranking the mined trajectory patterns and diversifying the ranking results. Through leveraging the relationships among users, locations and trajectories, we rank the trajectory patterns. We then use an exemplar-based algorithm to diversify the results in order to discover the representative trajectory patterns. We have evaluated the proposed framework on 12 different cities using a Flickr dataset and demonstrated its effectiveness.
AB - Social media such as those residing in the popular photo sharing websites is attracting increasing attention in recent years. As a type of user-generated data, wisdom of the crowd is embedded inside such social media. In particular, millions of users upload to Flickr their photos, many associated with temporal and geographical information. In this paper, we investigate how to rank the trajectory patterns mined from the uploaded photos with geotags and timestamps. The main objective is to reveal the collective wisdom recorded in the seemingly isolated photos and the individual travel sequences reflected by the geo-tagged photos. Instead of focusing on mining frequent trajectory patterns from geo-tagged social media, we put more effort into ranking the mined trajectory patterns and diversifying the ranking results. Through leveraging the relationships among users, locations and trajectories, we rank the trajectory patterns. We then use an exemplar-based algorithm to diversify the results in order to discover the representative trajectory patterns. We have evaluated the proposed framework on 12 different cities using a Flickr dataset and demonstrated its effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=84860165017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860165017&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972818.84
DO - 10.1137/1.9781611972818.84
M3 - Conference contribution
AN - SCOPUS:84860165017
SN - 9780898719925
T3 - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
SP - 980
EP - 991
BT - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PB - Society for Industrial and Applied Mathematics Publications
T2 - 11th SIAM International Conference on Data Mining, SDM 2011
Y2 - 28 April 2011 through 30 April 2011
ER -