Social recommender systems for web 2.0 folksonomies Export

@inproceedings{citeulike:5032574, 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.}, address = {New York, NY, USA}, author = {Siersdorfer, Stefan and Sizov, Sergej}, booktitle = {HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia}, citeulike-article-id = {5032574}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557914.1557959}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557914.1557959}, doi = {10.1145/1557914.1557959}, isbn = {978-1-60558-486-7}, keywords = {folksonomy, recommender, tagging}, location = {Torino, Italy}, pages = {261--270}, posted-at = {2009-07-01 14:32:24}, priority = {3}, publisher = {ACM}, title = {Social recommender systems for web 2.0 folksonomies}, url = {http://dx.doi.org/10.1145/1557914.1557959}, year = {2009} }

TY - CONF ID - citeulike:5032574 L3 - citeulike-article-id:5032574 N2 - 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. CY - Torino, Italy SN - 978-1-60558-486-7 AD - New York, NY, USA EP - 270 TI - Social recommender systems for web 2.0 folksonomies PB - ACM T2 - HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia SP - 261 KW - folksonomy KW - recommender KW - tagging AU - Siersdorfer, Stefan AU - Sizov, Sergej PY - 2009/// UR - http://dx.doi.org/10.1145/1557914.1557959 ER -