Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation
S. Doerfel, R. Jäschke, A. Hotho, и G. Stumme.
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, стр. 9--16. New York, NY, USA, ACM, (Сентябрь 2012)

The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.

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http://doi.acm.org/10.1145/2365934.2365937

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@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  added-at = {2013-03-18T14:06:44.000+0100},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  biburl = {https://puma.uni-kassel.de/bibtex/264bf590675a833770b7d284871435a8d/stumme},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {64bf590675a833770b7d284871435a8d},
  isbn = {978-1-4503-1638-5},
  keywords = {2012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging},
  location = {Dublin, Ireland},
  month = sep,
  pages = {9--16},
  publisher = {ACM},
  timestamp = {2013-03-18T14:06:48.000+0100},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation },
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}

%0 Conference Paper
%1 doerfel2012leveraging
%A Doerfel, Stephan
%A Jäschke, Robert
%A Hotho, Andreas
%A Stumme, Gerd
%B Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
%C New York, NY, USA
%D 2012
%I ACM
%K 2012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging
%P 9--16
%R 10.1145/2365934.2365937
%T Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation
%U http://doi.acm.org/10.1145/2365934.2365937
%X The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
%@ 978-1-4503-1638-5

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