Abstract
Much work has been done on supervised ranking for information retrieval, where the goal is to rank all searched documents in a known repository with many labeled query-document pairs. Unfortunately, the labeled pairs are lack because human labeling is often expensive, difficult and time consuming. To address this issue, we employ graph to represent pairwise relationships among the labeled and unlabeled documents, in order that the ranking score can be propagated to their neighbors. Our main contribution in this paper is to propose a semi-supervised ranking method based on graph-ranking and different weighting schemas. Experimental results show that our method called SSG-Rank on 20-newsgroups dataset outperforms supervised ranking (Ranking SVM and PRank) and unsupervised graph ranking significantly.
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References
Agarwal, S.: Ranking on Graph Data. In: The proceedings of International Conference of Machine Learning 2006, pp. 25–32 (2006)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Brin, S., Page, L.: The Anatomy of a Large Scale Hypertextual Web Search Engine. In: Proceedings of 7th International World Wide Web Conference, pp. 107–117 (1998)
Cao, Y., Xu, J., Liu, T.Y., Li, H., Huang, Y.L., Hon, H.W., Adapting Ranking, S.V.M.: to Document Retrieval. In: Proceedings of ACM SIGIR, vol. 29, pp. 186–193 (2006)
Crammer, K., Singer, Y.: PRanking with ranking. Advances in Neural Information Processing Systems, Canada (2002)
Herbrich, R., Graepel, T., Obermayer, K.: Large Margin Rank Boundaries for Ordinal Regression, Advances in Large Margin Classifiers, pp. 115–132. MIT Press, Cambridge (2000)
Joachims, T.: Transductive inference for text classification using support vector machine. In: Proceedings of 16th International Conference of Machine Learning, pp. 200–209 (1999)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms, New Orleans, pp. 668–677 (1997)
Liu, T., Xu, J., Qin, T., Xiong, W., Li, H.: LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval. In: SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (2007)
Robertson, S., Hull, D.: The TREC-9 filtering track final report. In: TREC, pp. 25–40 (2000)
Wan, X., Yang, J., Xiao, J.: Document Similarity Search Based on Manifold- Ranking of TextTiles. In: The 3rd Asia Information Retrieval Symposium, Singapore, pp. 14–25 (2006)
Wang, F., Zhang, C.: Label Propagation Through Linear Neighborhoods. In: Proceedings of 23rd International Conference of Machine Learning, pp. 985–992 (2006)
Xu, J., Cao, Y., Li, H., Huang, Y.: Cost-Sensitive Learning of SVM for Ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 833–840. Springer, Heidelberg (2006)
Xu, J., Li, H.: AdaRank: A Boosting Algorithm for Information Retrieval. In: The proceedings of SIGIR 2007, pp. 391–398 (2007)
Zhou, D.Y., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: Advances in Neural Information Processing Systems 16, pp. 321–328 (2004)
Zhou, D.Y., Weston, J., Gretton, A., et al.: Ranking on Data Manifolds. In: Advances in Neural Information Processing System 16 (2003)
Zhou, Z.H., Li, M.: Semi-supervised regression with co-training. In: Proceedings of International Joint Conference on Artificial Intelligence 2005 (2005)
Zhu, X.J.: Semi-Supervised Learning Literature Survey, Computer Sciences Technical Report 1530, University of Wisconsin-Madison (2005)
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Xie, M., Liu, J., Zheng, N., Li, D., Huang, Y., Wang, Y. (2008). Semi-Supervised Graph-Ranking for Text Retrieval. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_25
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DOI: https://doi.org/10.1007/978-3-540-68636-1_25
Publisher Name: Springer, Berlin, Heidelberg
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