Abstract
In this chapter, we introduce query-dependent ranking. Considering the large differences between different queries, it might not be the best choice to use a single ranking function to deal with all kinds of queries. Instead, one may achieve performance gain by leveraging the query differences. To consider the query difference in training, one can use a query-dependent loss function. To further consider the query difference in the test process, a query-dependent ranking function is needed. Several ways of learning a query-dependent ranking function are reviewed in this chapter, including query classification-based approach, query clustering-based approach, nearest neighbor-based approach, and two-layer learning-based approach. Discussions are also made on the future research directions along this line.
This is a preview of subscription content, log in via an institution to check access.
References
Banerjee, S., Dubey, A., Machichihar, J., Chakrabarti, S.: Efficient and accurate local learning for ranking. In: SIGIR 2009 Workshop on Learning to Rank for Information Retrieval (LR4IR 2009) (2009)
Bian, J., Liu, T.Y., Qin, T., Zha, H.: Ranking with query-dependent loss for web search. In: Proceedings of the 3rd International Conference on Web Search and Web Data Mining (WSDM 2010), pp. 141–150 (2010)
Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)
Burges, C.J., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 89–96 (2005)
Geng, X.B., Liu, T.Y., Qin, T., Li, H., Shum, H.Y.: Query-dependent ranking using k-nearest neighbor. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 115–122 (2008)
He, Y., Liu, T.Y.: Query-dependent ranking using couplenet. Tech. rep., Microsoft Research (2010)
Herbrich, R., Obermayer, K., Graepel, T.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers, pp. 115–132 (2000)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 133–142 (2002)
Kang, I., Kim, G.: Query type classification for web document retrieval. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003), pp. 64–71 (2003)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Xia, F., Liu, T.Y., Wang, J., Zhang, W., Li, H.: Listwise approach to learning to rank—theorem and algorithm. In: Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp. 1192–1199 (2008)
Zha, H., Zheng, Z., Fu, H., Sun, G.: Incorporating query difference for learning retrieval functions in world wide web search. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM 2006), pp. 307–316 (2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liu, TY. (2011). Query-Dependent Ranking. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-14267-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14266-6
Online ISBN: 978-3-642-14267-3
eBook Packages: Computer ScienceComputer Science (R0)