Automatic tag recommendation algorithms for social recommender systems

ACM Transactions on the Web

Volumn 5, Issue 1, 2011, Pages

  Song, Yang ^a   Zhang, Lu ^b   Giles, C Lee ^b  

^a MICROSOFT RESEARCH   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Gaussian processes; Graph partitioning; Mixture model; Multi label classification; Prototype selection; Tagging system

Indexed keywords

GAUSSIAN PROCESSES; GRAPH PARTITIONING; MIXTURE MODEL; MULTI-LABEL; PROTOTYPE SELECTION; TAGGING SYSTEM;

ALGORITHMS; CLASSIFIERS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GRAPH THEORY; MODELS; RECOMMENDER SYSTEMS; THESAURI; USER INTERFACES; WORLD WIDE WEB;

INFORMATION RETRIEVAL SYSTEMS;

EID: 79951867458     PISSN: 15591131     EISSN: 1559114X     Source Type: Journal    
DOI: 10.1145/1921591.1921595     Document Type: Article

References (31)

1. BEGELMAN, G., KELLER, P., AND SMADJA, F. 2006. Automated tag clustering: Improving search and exploration in the tag space. In Proceedings of the Collaborative Web Tagging Workshop (WWW'06).
2. BLEI, D. M., NG, A. Y., AND JORDAN, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022.
3. BOGERS, T. AND VAN DEN BOSCH, A. 2008. Recommending scientific articles using citeulike. In Proceedings of the ACM Conference on Recommender Systems (RecSys'08). ACM, New York, NY, 287-290.
4. BREESE, J. S., HECKERMAN, D., AND KADIE, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 43-52.
5. BRINKER, K., FURNKRANZ, J., AND HULLERMEIER, E. 2006. A unified model for multilabel classification and ranking. In Proceedings of the European Conference on Artificial Intelligence (ECAI'06).
6. CHIRITA, P. A., COSTACHE, S., NEJDL, W., AND HANDSCHUH, S. 2007. P-tag: large scale automatic generation of personalized annotation tags for the web. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). ACM Press, New York, NY, 845-854. (Pubitemid 47582314)
7. CRISTIANINI, N. AND SHAWE-TAYLOR, J. 2000. An Introduction to Support Vector Machines and Other Kernelbased Learning Methods. Cambridge University Press.
8. DEMPSTER, A. P., LAIRD, N. M., AND RUBIN, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Sens B, 39, 1, 1-38.
9. FAROOQ, U., SONG, Y., CARROLL, J. M., AND GILES, C. L. 2007. Social bookmarking for scholarly digital libraries. IEEE Internet Comput. 29-35. (Pubitemid 350148361)
10. FIGUEIREDO, M. A. T. AND JAIN, A. K. 2002. Unsupervised learning of finite mixture models. IEEE Trans. Patt. Anal. Mach. Intell. 24, 3, 381-396. (Pubitemid 35289353)
11. GOLDER, S. AND HUBERMAN, B. 2006. Usage patterns of collaborative tagging systems. J. Inform. Sci.
12. GOLUB, G. H. AND LOAN, C. F. V. 1996. Matrix Computations 3rd Ed. Johns Hopkins University Press.
13. JAESCHKE, R., MARINHO, L., HOTHO, A., SCHMIDT-THIEME, L., AND STUMME, G. 2007. Tag recommendations in folksonomies. In Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivitt (LWA'07). Martin-Luther-Universität Halle-Wittenberg, 13-20.
14. JOHNSON, R. AND ZHANG, T. 2007. On the effectiveness of laplacian normalization for graph semi-supervised learning. J. Mach. Learn. Res. 8, 1489-1517. (Pubitemid 47210274)
15. KENDALL, M. 1938. A new measure of rank correlation. Biometrika 30, 81-89.
16. KOHONEN, T. 2001. Self Organization Maps. Springer.
17. KULLBACK, S. AND LEIBLER, R. A. 1951. On information and sufficiency. Annl. Math. Stat. 22, 79-86.
18. LAWRENCE, N., SEEGER, M., AND HERBRICH, R. 2003. Fast sparse gaussian process methods: The informative vector machine. In Proceedings of Neural Information Processing Systems (NIPS15). 609-616.
19. LI, J. AND WANG, J. Z. 2006. Real-time computerized annotation of pictures. In Proceedings of the International Conference on Multimedia (MULTIMEDIA'06). 911-920. (Pubitemid 47113653)
20. LI, J. AND ZHA, H. 2006. Two-way poisson mixture models for simultaneous document classification and word clustering. Comput. Stat. Data Anal. 50, 1.
21. PLATT, J. C. 2000. Probabilities for SV machines. In Advances in Large Margin Classifiers, 61-74.
22. RASMUSSEN, C. E. AND WILLIAMS, C. K. I. 2006. Gaussian Processes for Machine Learning. MIT Press.
23. SCHLATTMANN, P. 2003. Estimating the number of components in a finite mixture model: the special case of homogeneity. Comput. Stat. Data Anal. 41, 3-4, 441-451. (Pubitemid 36065388)
24. SEEGER, M. AND JORDAN, M. 2004. Sparse gaussian process classification with multiple classes. Tech. rep. 661, Department of Statistics, University of California at Berkeley.
25. SEEGER, M. AND WILLIAMS, C. 2003. Fast forward selection to speed up sparse gaussian process regression. In Proceedings of the Workshop on AI and Statistics.
26. SEO, S., BODE, M., AND OBERMAYER, K. 2003. Soft nearest prototype classification. IEEE Trans. Neural Net.
27. SONG, Y., ZHANG, L., AND GILES, C. L. 2008. Sparse gaussian processes classification for fast tag recommendation. In Proceedings of the 17th ACM Conference on Information and KnowledgeManagement (CIKM'08). ACM, New York, NY.
28. SONG, Y., ZHUANG, Z., LI, H., ZHAO, Q., LI, J., LEE, W.-C., AND GILES, C. L. 2008. Real-time automatic tag recommendation. In Proceedings of the Annual International ACM SIGIR Conference (SIGIR'08).
29. SYMEONIDIS, P., NANOPOULOS, A., AND MANOLOPOULOS, Y. 2008. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the ACM Conference on Recommender Systems (RecSys'08). ACM, New York, NY, 43-50.
30. TSOUMAKAS, G. AND KATAKIS, I. 2007. Multi-label classification: An overview. Intl. J.DataWarehous. Mining 3, 3, 1-13.
31. ZHA, H., HE, X., DING, C., SIMON, H., AND GU, M. 2001. Bipartite graph partitioning and data clustering. In Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM'01). ACM Press, New York, NY, 25-32. (Pubitemid 34974610)