Web search personalization via social bookmarking and tagging
M. Noll, und C. Meinel.
Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, Volume 4825 von LNCS, Seite 365--378. Berlin, Heidelberg, Springer Verlag, (November 2007)

In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.

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@inproceedings{Noll/2007/search,
  abstract = {In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.},
  added-at = {2008-06-12T08:19:04.000+0200},
  address = {Berlin, Heidelberg},
  author = {Noll, Michael and Meinel, Christoph},
  biburl = {https://puma.uni-kassel.de/bibtex/252943a6298169f5a552bffbbee352937/hotho},
  booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
  crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
  editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe},
  interhash = {8c1f9db1455effa2cdf949c0191a31d2},
  intrahash = {52943a6298169f5a552bffbbee352937},
  keywords = {bookmarking iswc search social tagging taggingsurvey toread},
  month = {November},
  pages = {365--378},
  publisher = {Springer Verlag},
  series = {LNCS},
  timestamp = {2012-01-25T16:44:27.000+0100},
  title = {Web search personalization via social bookmarking and tagging},
  url = {http://iswc2007.semanticweb.org/papers/365.pdf},
  volume = 4825,
  year = 2007
}

%0 Conference Paper
%1 Noll/2007/search
%A Noll, Michael
%A Meinel, Christoph
%B Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea
%C Berlin, Heidelberg
%D 2007
%E Aberer, Karl
%E Choi, Key-Sun
%E Noy, Natasha
%E Allemang, Dean
%E Lee, Kyung-Il
%E Nixon, Lyndon J B
%E Golbeck, Jennifer
%E Mika, Peter
%E Maynard, Diana
%E Schreiber, Guus
%E Cudré-Mauroux, Philippe
%I Springer Verlag
%K bookmarking iswc search social tagging taggingsurvey toread
%P 365--378
%T Web search personalization via social bookmarking and tagging
%U http://iswc2007.semanticweb.org/papers/365.pdf
%V 4825
%X In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.

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