Large-scale item categorization for e-commerce

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 595-604

  Shen, Dan ^a   Ruvini, Jean David ^b   Sarwar, Badrul ^b  

^a dianpingcom   (China)

^b EBAY INC   (United States)

Author keywords

classification; text

Indexed keywords

CLASSIFICATION ALGORITHM; E-COMMERCE SITES; EXPERIMENTAL EVALUATION; FEATURE SPACE; GRAPH ALGORITHMS; HIERARCHICAL APPROACH; ILLUSTRATIVE EXAMPLES; INDUSTRIAL SETTINGS; TEXT; TEXT CATEGORIZATION; TRAINING DATA;

ALGORITHMS; DIGITAL STORAGE; ELECTRONIC COMMERCE; KNOWLEDGE MANAGEMENT; TEXT PROCESSING;

CLASSIFICATION (OF INFORMATION);

EID: 84871042856     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2396838     Document Type: Conference Paper

References (29)

1. C. Apté, F. Damerau, and S. M. Weiss. Automated learning of decision rules for text categorization. ACM Trans. Inf. Syst., 12:233-251, 1994.
2. S. Bengio, J. Weston, and D. Grangier. Label embedding trees for large multi-class tasks. In Advances in Neural Information Processing Systems (NIPS), 2010.
3. L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In Proc. of the 13th ACM International Conference on Information and Knowledge Management(CIKM), pages 78-87, 2004.
4. O. Dekel, J. Keshet, and Y. Singer. Large margin hierarchical classification. In Proc. of the 21st International Conference on Machine Learning(ICML), pages 27-34, 2004.
5. T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Artificial Intelligence Research, 2:263-286, 1995.
6. S. T. Dumais and H. Chen. Hierarchical classification of web content. In Proc. of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 256-263, 2000.
7. R. E. Fan, K.W. Chang, C. J. Hsieh, X. R.Wang, and C. J. Lin. Liblinear: A library for large linear classification. Journal of Machine Learning Research, pages 1871-1874, 2008.
8. Q. Y. G.-R. Xue, D. Xing and Y. Yu. Deep classification in large-scale text hierarchies. In Proc. of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 619-626, 2008.
9. A. V. Goldberg. Finding a maximum density subgraph. Technical Report UCB/CSD-84-171, EECS Department, University of California, Berkeley, 1984.
10. T. Hastie and R. Tibshirani. Classification by pairwise coupling. Annals of Statistics, 26:451-471, 1998.
11. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proc. of the 10th European Conference on Machine Learning(ECML), pages 137-142, 1998.
12. D. Koller and M. Sahami. Hierarchically classifying docuemnts using very few words. In Proc. of the 14th International Conference on Machine Learning(ICML), pages 171-178, 1997.
13. S. Kumar, J. Ghosh, and M. M. Crawford. Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis and Applications, 5:210-220, 2002.
14. A. Macallum and K. Nigam. A comparison of event models for naive bayes text classification. In Proc. of the AAAI-98 Workshop on Learning for Text Categorization, 1998.
15. A. McCallum, R. Rosenfeld, T. Mitchell, and A. Y. Ng. Improving text classification by shrinkage in a hierarchy of classes. In Proc. of the 15th International Conference on Machine Learning(ICML), pages 359-367, 1998.
16. K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. In Proc. of IJCAI-99 Workshop on Machine Learning for Information Filtering, pages 61-67, 1999.
17. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61-74. MIT Press, 1999.
18. R. Rifkin and A. Klautau. In defense of one-vs-all classification. Machine Learning Research, 5:101-141, 2004.
19. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34:1-47, 2002.
20. D. Shen, J. Ruvini, M. Somaiya, and N. Sundaresan. Item categorization in the e-commerce domain. In Proc. of The 20th ACM International Conference on Information and Knowledge Management (CIKM), 2011.
21. D. Shen, J. D. Ruvini, R. Mukherjee, and N. Sundaresan. A study of smoothing algorithms for item categorization on e-commerce sites. In Proc. of The Ninth International Conference on Machine Learning and Applications(ICMLA), 2010.
22. C. N. Silla and A. A. Freitas. A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22:31-72, 2011.
23. A. S.Weigend, E. D.Wiener, and J. O. Pedersen. Exploiting hierarchy in text categorization. Information Retrieval, pages 193-216, 1999.
24. R. Tarjan. Depth-first search and linear graph algorithms. SIAM Journal on Computing, 1(2):146-160, 1972.
25. V. N. Vapnik. Statistical Learning Theory, 1998.
26. K. Weinberger and O. Chapelle. Large margin taxonomy embedding with an application to document categorization. Advances in Neural Information Processing Systems, pages 1737-1744, 2008.
27. T. yan Liu, Y. Yang, H. Wan, H. jun Zeng, Z. Chen, and W. ying Ma. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations, 7:2005, 2005.
28. Y. Yang and X. Liu. A re-examination of text categorization methods. In Proc. of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42-49, 1999.
29. Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proc. of the 14th International Conference on Machine Learning(ICML), pages 412-420, 1997.