TY - GEN
T1 - On theme location discovery for travelogue services
AU - Ye, Mao
AU - Xiao, Rong
AU - Lee, Wang Chien
AU - Xie, Xing
PY - 2011
Y1 - 2011
N2 - In this paper, we aim to develop a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users. In this service, theme locations in a travelogue are the core information to discover. Thus we aim to address the problem of theme location discovery to enable the above travelogue services. Due to the inherent ambiguity of location relevance, we perform location relevance mining (LRM) in two complementary angles, relevance classification and relevance ranking, to provide comprehensive understanding of locations. Furthermore, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to develop a co-training model for enhancement of classification performance. Built upon the mining result of LRM, we develop a series of techniques for provisioning of the aforementioned travelogue digests in our travelogue system. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance mining techniques and demonstrate the effectiveness of the travelogue service.
AB - In this paper, we aim to develop a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users. In this service, theme locations in a travelogue are the core information to discover. Thus we aim to address the problem of theme location discovery to enable the above travelogue services. Due to the inherent ambiguity of location relevance, we perform location relevance mining (LRM) in two complementary angles, relevance classification and relevance ranking, to provide comprehensive understanding of locations. Furthermore, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to develop a co-training model for enhancement of classification performance. Built upon the mining result of LRM, we develop a series of techniques for provisioning of the aforementioned travelogue digests in our travelogue system. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance mining techniques and demonstrate the effectiveness of the travelogue service.
UR - http://www.scopus.com/inward/record.url?scp=80052109629&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052109629&partnerID=8YFLogxK
U2 - 10.1145/2009916.2009980
DO - 10.1145/2009916.2009980
M3 - Conference contribution
AN - SCOPUS:80052109629
SN - 9781450309349
T3 - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 465
EP - 474
BT - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
Y2 - 24 July 2011 through 28 July 2011
ER -