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Social Recommender Systems for Web 2.0 Folksonomies

by: Stefan Siersdorfer, and Sergej Sizov

In: HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia New York, NY, USA: ACM (July 2009) .

Abstract

The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.

BibTeX record

@inproceedings{siersdorfer2009social,
  abstract = {The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.},
  added-at = {2009-06-29T11:01:24.000+0200},
  address = {New York, NY, USA},
  author = {Siersdorfer, Stefan and Sizov, Sergej},
  biburl = {http://www.bibsonomy.org/bibtex/2bbf0c98e0ab32612109e6688de81c432/jaeschke},
  booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
  interhash = {9245d0a556113aa107ba8171f3897156},
  intrahash = {bbf0c98e0ab32612109e6688de81c432},
  keywords = {folksonomy recommender social},
  month = {July},
  paperid = {fp091},
  publisher = {ACM},
  session = {Full Paper},
  timestamp = {2009-06-29T11:01:24.000+0200},
  title = {Social Recommender Systems for Web 2.0 Folksonomies},
  year = 2009
}

Endnote record

%0 Conference Paper
%1 siersdorfer2009social
%A Siersdorfer, Stefan
%A Sizov, Sergej
%B HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia
%C New York, NY, USA
%D 2009
%I ACM
%K 
%T Social Recommender Systems for Web 2.0 Folksonomies
%X The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.