Tag-aware recommender systems by fusion of collaborative filtering algorithms

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2008, Pages 1995-1999

  Tso Sutter, Karen H L ^a   Marinho, Leandro Balby ^a   Schmidt Thieme, Lars ^a  

^a UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

Collaborative filtering; Recomniender systems; Tags

Indexed keywords

INFORMATION RETRIEVAL SYSTEMS; METADATA; RAPID SOLIDIFICATION; THREE DIMENSIONAL;

ADDITIONAL KNOWLEDGES; COLLABORATIVE FILTERING; COLLABORATIVE FILTERING ALGORITHMS; CONTENT INFORMATIONS; DATA SETS; EMPIRICAL EVALUATIONS; FUSION METHODS; GENERIC METHODS; RECOMMENDATION QUALITIES; RECOMMENDER SYSTEMS; RECOMNIENDER SYSTEMS; RS ALGORITHMS; TAGGING SYSTEMS; TAGS; TWODIMENSIONAL CORRELATIONS;

SIGNAL FILTERING AND PREDICTION;

EID: 56749171376     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1363686.1364171     Document Type: Conference Paper

References (22)

1. G. Adomavicius. R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst., 23(1):103-145, 2005.
2. M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Commun. ACM. 40:66-72. 1997.
3. J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning, pages 65-72, New York, NY, USA, 2004. ACM Press.
4. C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: using social and content-based information in recommendation. In AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, pages 714-720, Menlo Park, CA, USA, 1998. American Association for Artificial Intelligence.
5. D. Benz, K. Tso, and L. Schmidt-Thieme. Automatic bookmark classification: A collaborative approach. In Proceedings of the Second Workshop on Innovations in Web Infrastructure (IWI 2006), Edinburgh, Scotland, 2006.
6. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). G. F. Cooper, and S. Moral, Eds. Morgan-Kaufmann, San Francisco, Calif, pages 43-52, 1998.
7. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12:331-370, 2002.
8. M. Claypool, A. Gokhale. T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of A CM SIGIR. Workshop on Recommender Systems, August 1999.
9. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems. Springer-Verlag, 22/1, 2004.
10. D. Goldberg, D. Nichols, B. M. Oki. and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, 1992.
11. R. Jaeschke, L. Marinho, A. Hotho,L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland (to appear), 2007.
12. Q. Lin and B. M. Kim. An approach for combining content-based and collaborative filters. In Proceedings of the Sixth international workshop on Information retrieval with Asian languages (ACL-2003), pages 17-24. 2003.
13. L. B. Marinho and L. Schmidt-Thieme. Collaborative tag recommendations. In Proceedings of 31st Annual Conference of the Gesellschaft fr Klassifikation (GfKl), Freiburg (to appear). Springer, 2007.
14. P. Melville. R. Mooney, and R. Nagarajan. Content-boosted collaborative filtering. In Proceedings of Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pages 187-192, 2001.
15. G. Mishne. Autotag: a collaborative approach to automated tag assignment for weblog posts. In WWW '06: Proceedings of the 15th international conference, on World Wide Web, pages 953-954. New York. NY. USA, 2006. ACM Press.
16. M. J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev., 13(5-6) :393-408, 1999.
17. A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 437-444, 2001.
18. P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the ACM1994 Conference on Computer Supported Cooperative Work, pages 175-186, Chapel Hill. North Carolina, 1994. ACM.
19. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of the Second ACM Conference on Electronic Commerce (ECOO), pages 285-295. 2000.
20. K. Tso and L. Schmidt-Thieme. Evaluation of attribute-aware recommender system algorithms on data with varying characteristics. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), pages 831-840. Springer, 2006.
21. J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR. '06: Proceedings of the 29th annual international ACM SIGIR. conference on Research and development in information retrieval, pages 501-508, New York, NY, USA, 2006. ACM Press.
22. Z. Xu. Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. In Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006, Edinburgh, Scotland, 2006.