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Detecting Trends in Social Bookmarking Systems using a Probabilistic Generative Model and Smoothing

by: Robert Wetzker, Till Plumbaum, Alexander Korth, Christian Bauckhage, Tansu Alpcan, and Florian Metze

In: Proceedings of the International Conference on Pattern Recognition ICPR Tampa, Florida, USA: (2008) .

Abstract

We propose a method for the detection of trends in social bookmarking systems. Compared to other work in this emerging field, our approach has a more sound statistical basis. In order to cope with the problem of vanishing probabilities due to data sparsity, we apply smoothing and show that it allows for an easy calibration of our trend detector resulting in better generalization and scalability. We test our approach on a collection of 105, 000, 000 bookmarks collected from the del.icio.us bookmarking service. To our knowledge, this is the largest corpus of a real world bookmarking service analyzed in this context. The results show that our method outperforms previously proposed methods and successfully detects trends in the data.

BibTeX record

@inproceedings{wetzker2008detecting,
  abstract = {We propose a method for the detection of trends in social bookmarking systems. Compared to other work in this emerging field, our approach has a more sound statistical basis. In order to cope with the problem of vanishing probabilities due to data sparsity, we apply smoothing and show that it allows for an easy calibration of our trend detector resulting in better generalization and scalability. We test our approach on a collection of 105, 000, 000 bookmarks collected from the del.icio.us bookmarking service. To our knowledge, this is the largest corpus of a real world bookmarking service analyzed in this context. The results show that our method outperforms previously proposed methods and successfully detects trends in the data.},
  added-at = {2009-11-09T23:57:28.000+0100},
  address = {Tampa, Florida, USA},
  author = {Wetzker, Robert and Plumbaum, Till and Korth, Alexander and Bauckhage, Christian and Alpcan, Tansu and Metze, Florian},
  biburl = {http://www.bibsonomy.org/bibtex/2ec6d53fe818eecbf54de78a5ac994ae0/megmed},
  booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR)},
  interhash = {b0c8d132942d53cd4f1db8a3190c18d5},
  intrahash = {ec6d53fe818eecbf54de78a5ac994ae0},
  keywords = {analysis delicious folksonomy trend},
  timestamp = {2009-11-09T23:57:28.000+0100},
  title = {Detecting Trends in Social Bookmarking Systems using a Probabilistic Generative Model and Smoothing},
  year = 2008
}

Endnote record

%0 Conference Paper
%1 wetzker2008detecting
%A Wetzker, Robert
%A Plumbaum, Till
%A Korth, Alexander
%A Bauckhage, Christian
%A Alpcan, Tansu
%A Metze, Florian
%B Proceedings of the International Conference on Pattern Recognition (ICPR)
%C Tampa, Florida, USA
%D 2008
%K 
%T Detecting Trends in Social Bookmarking Systems using a Probabilistic Generative Model and Smoothing
%X We propose a method for the detection of trends in social bookmarking systems. Compared to other work in this emerging field, our approach has a more sound statistical basis. In order to cope with the problem of vanishing probabilities due to data sparsity, we apply smoothing and show that it allows for an easy calibration of our trend detector resulting in better generalization and scalability. We test our approach on a collection of 105, 000, 000 bookmarks collected from the del.icio.us bookmarking service. To our knowledge, this is the largest corpus of a real world bookmarking service analyzed in this context. The results show that our method outperforms previously proposed methods and successfully detects trends in the data.