Geodesic
Unified Classification Model for Geotagging Websites
Modelirovanie i analiz informacionnyh sistem, Tome 20 (2013) no. 2, pp. 80-91.

Voir la notice de l'article provenant de la source Math-Net.Ru

The paper presents a novel approach to finding regional scopes (geotagging) of websites. Unlike the traditional approaches, which generally involve training a separate classification model for each class (region), the proposed method is based on training a single model which is used for all regions of the same type (e.g. cities). This approach is made possible by the usage of “relative” features which indicate how a selected region matches up to other regions for a given website. The classification system uses a variety of features of different nature that have not been yet used together for machine-learning based regional classification of websites. The evaluation demonstrates the advantage of our “one model per region type” method versus the traditional “one model per region” approach. A separate experiment demonstrates the ability of the proposed classifier to successfully detect regions which were not present in the training set (which is impossible for traditional approaches).
Keywords: geotagging, classification models, machine learning. 
@article{MAIS_2013_20_2_a5,
     author = {A. N. Volkov},
     title = {Unified {Classification} {Model} for {Geotagging} {Websites}},
     journal = {Modelirovanie i analiz informacionnyh sistem},
     pages = {80--91},
     publisher = {mathdoc},
     volume = {20},
     number = {2},
     year = {2013},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MAIS_2013_20_2_a5/}
}
TY  - JOUR
AU  - A. N. Volkov
TI  - Unified Classification Model for Geotagging Websites
JO  - Modelirovanie i analiz informacionnyh sistem
PY  - 2013
SP  - 80
EP  - 91
VL  - 20
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MAIS_2013_20_2_a5/
LA  - ru
ID  - MAIS_2013_20_2_a5
ER  - 
%0 Journal Article
%A A. N. Volkov
%T Unified Classification Model for Geotagging Websites
%J Modelirovanie i analiz informacionnyh sistem
%D 2013
%P 80-91
%V 20
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MAIS_2013_20_2_a5/
%G ru
%F MAIS_2013_20_2_a5
A. N. Volkov. Unified Classification Model for Geotagging Websites. Modelirovanie i analiz informacionnyh sistem, Tome 20 (2013) no. 2, pp. 80-91. http://geodesic.mathdoc.fr/item/MAIS_2013_20_2_a5/

[1] E. Amitay, N. Har'El, R. Sivan, A. Soffer, “Web-a-where: geotagging web content”, ACM SIGIR (2004), 273–280

[2] Z. Cheng, J. Caverlee, K. Lee, “You are where you tweet: a content-based approach to geo-locating twitter users”, CIKM (2010), 759–768

[3] D. J. Crandall, L. Backstrom, D. Huttenlocher, J. Kleinberg, “Mapping the world's photos”, WWW ACM (2009), 761–770

[4] J. Ding, L. Gravano, N. Shivakumar, “Computing geographical scopes of web resources”, VLDB (2000)

[5] A. Gulin, P. Karpovich, Greedy function optimization in learning to rank, 2009

[6] T.-Y. Liu, “Learning to rank for information retrieval”, Foundations and Trends in Information Retrieval, 2009

[7] A. Pyalling, M. Maslov, P. Braslavski, “Automatic geotagging of russian web sites”, WWW (2006), 965–966

[8] A. Pyalling, M. Maslov, S. Trifonov, “Automatic classification of websites”, RCDL (2008)

[9] X. Qi, B. D. Davison, “Web page classification: Features and algorithms”, ACM Comput. Surv., 41 (2009) | Zbl

[10] P. Serdyukov, V. Murdock, R. van Zwol, “Placing flickr photos on a map”, SIGIR (2009), 484–491

[11] W. Zong, D. Wu, A. Sun, E.-P. Lim, D. H.-L. Goh, “On assigning place names to geography related web pages”, JCDL ACM (2005), 354–362