A User Group Clustering Approach in Tagging Systems
R. Pan, G. Xu, and P. Dolog.. Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet, Kassel, Germany, (2010)
Abstract
In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.
%0 Conference Paper
%1 abis1
%A Pan, Rong
%A Xu, Guandong
%A Dolog., Peter
%B Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet
%C Kassel, Germany
%D 2010
%E Atzmüller, Martin
%E Benz, Dominik
%E Hotho, Andreas
%E Stumme, Gerd
%K clustering document group metric modularity profile room:0425 session:abis2 spectral user workshop:abis
%T A User Group Clustering Approach in Tagging Systems
%U http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/abis1.pdf
%X In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.
@inproceedings{abis1,
abstract = {In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.},
added-at = {2010-10-05T14:15:12.000+0200},
address = {Kassel, Germany},
author = {Pan, Rong and Xu, Guandong and Dolog., Peter},
biburl = {https://www.bibsonomy.org/bibtex/25804c8075696f1b51f7a61d623a1ba11/lwa2010},
booktitle = {Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaet},
crossref = {lwa2010},
editor = {Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
interhash = {6f697e4e1cae06fb44a39b85ead97bae},
intrahash = {5804c8075696f1b51f7a61d623a1ba11},
keywords = {clustering document group metric modularity profile room:0425 session:abis2 spectral user workshop:abis},
presentation_end = {2010-10-05 16:45:00},
presentation_start = {2010-10-05 16:00:00},
room = {0425},
session = {abis2},
timestamp = {2010-10-05T14:15:13.000+0200},
title = {A User Group Clustering Approach in Tagging Systems},
track = {abis},
url = {http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/abis1.pdf},
year = 2010
}