Social recommender systems for web 2.0 folksonomies

Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT'09

Volumn , Issue , 2009, Pages 261-269

  Siersdorfer, Stefan ^b   Sizov, Sergej ^a  

^a University of Koblenz   (Germany)

^b L3S RESEARCH CENTER   (Germany)

Author keywords

Algorithms; Experimentation; Human Factors

Indexed keywords

COMPREHENSIVE EVALUATION; CONTENT SHARING; CONTENT-SHARING SYSTEM; EXPERIMENTATION; FOLKSONOMIES; FORMAL MODEL; HUMAN FACTORS; META-KNOWLEDGE; RECOMMENDATION MECHANISM; RECOMMENDER SYSTEMS; SHARED DATA; SOCIAL ENVIRONMENT; SOCIAL RELATIONSHIPS; WEB 2.0; WEB 2.0 APPLICATIONS;

FLUORINE CONTAINING POLYMERS; HUMAN ENGINEERING; HYPERTEXT SYSTEMS; SEMANTIC WEB;

WORLD WIDE WEB;

EID: 70450164139     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557914.1557959     Document Type: Conference Paper

References (30)

1. G. Adomavicius and A. Tuzhilin. Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng., 17(6):734-749, 2005.
2. R. Albert and A. Barabasi. Statistical mechanics of complex networks. Review of Modern Physics, 74:47-97, 2001.
3. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
4. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-370, 2002.
5. K. Crammer and Y. Singer. Pranking with ranking. Advances in Neural Information Processing Systems, 14(1):641-647, 2002.
6. S. Deerwester, S. Dumais, T. Landauer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391U″407, 1990.
7. M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. In Proc. 15th Int. WWW Conference, May 2006.
8. G. Furnas, T. Landauer, L. Gomez, and S. Dumais. The vocabulary problem in human-system communication. Communications of the ACM, 30(1):964-971, 1987.
9. B. Ganter and R. Wille. Formal Concept Analysis: Mathematical foundations. Springer, 1999.
10. T. Hammond, T. Hannay, B. Lund, and J. Scott. Social Bookmarking Tools (I): A General Review. D-Lib Magazine, 11(4), April 2005.
11. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5-53, 2004.
12. T. Hofmann. Probabilistic latent semantic indexing. 22nd Annual International ACM SIGIR Conference, Berkeley, USA, pages 50-57, 1999.
13. A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information Retrieval in Folksonomies: Search and Ranking. In The Semantic Web: Research and Applications, volume 4011 of LNAI, pages 411-426, Heidelberg, 2006. Springer.
14. J. Kleinberg. Temporal dynamics of on-line information streams. In M. Garofalakis, J. Gehrke, and R. Rastogi, editors, Data Stream Management: Processing High-Speed Data Streams. Springer, 2006.
15. J. A. Konstan, J. Riedl, A. Borchers, and J. Herlocker. Recommender systems: A grouplens perspective. In Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08), pages 60-64. AAAI Press, 1998.
16. R. Lambiotte and M. Ausloos. Collaborative tagging as a tripartite network. ArXiv Computer Science e-prints, Dec. 2005.
17. F. Lehmann and R. Wille. A triadic approach to formal concept analysis. In Conceptual Structures: Applications, Implementation and Theory, volume 954 of Lecture Notes in Computer Science. Springer, 1995.
18. B. Lund, T. Hammond, M. Flack, and T. Hannay. Social Bookmarking Tools (II): A Case Study - Connotea. D-Lib Magazine, 11(4), 2005.
19. S. Middleton, N. Shadbolt, and D. De Roure. Ontological user profiling in recommender systems. ACM Trans. Inf. Syst., 22(1):54-88, 2004.
20. P. Mika. Ontologies are us: A unified model of social networks and semantics. 4th International Semantic Web Conference (ISWC), Galway, Ireland, pages 522-536, 2005.
21. M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167, 2003.
22. K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. Workshop on Machine Learning for Information Filtering, IJCAI'99, pages 61-67, 1999.
23. R. Pastor-Satorras and A. Vespignani. Evolution and Structure of the Internet: A Statistical Physics Approach. Cambridge University Press, New York, NY, USA, 2004.
24. B. J. Schafer, J. A. Konstan, and J. Riedl. Recommender systems in e-commerce. In ACM Conference on Electronic Commerce, pages 158-166, 1999.
25. C. Schmitz, A. Hotho, R. Jaeschke, and G. Stumme. Mining Association Rules in Folksonomies. pages 261-270, 2006.
26. G. Stumme. A finite state model for on-line analytical processing in triadic contexts. In ICFCA, pages 315-328, 2005.
27. J. Sun, D. Tao, and C. Faloutsos. Beyond streams and graphs: dynamic tensor analysis. ACM SIGKDD), Philadelphia, USA, pages 374-383, 2006.
28. S. van Dongen. A cluster algorithm for graphs. National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, Technical Report INS-R0010, 2000.
29. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
30. R. Wille. Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival, editor, Ordered Sets, pages 445-470. Reidel, Dordrecht-Boston, 1982.