Abstract
In this chapter, we summarize the entire book. In particular, we show the example algorithms introduced in this book in a figure. We then provide the answers to several important questions regarding learning to rank raised at the beginning of the book.
References
Burges, C.J., Ragno, R., Le, Q.V.: Learning to rank with nonsmooth cost functions. In: Advances in Neural Information Processing Systems 19 (NIPS 2006), pp. 395–402 (2007)
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)
Cao, Y., Xu, J., Liu, T.Y., Li, H., Huang, Y., Hon, H.W.: Adapting ranking SVM to document retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006), pp. 186–193 (2006)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 129–136 (2007)
Carvalho, V.R., Elsas, J.L., Cohen, W.W., Carbonell, J.G.: A meta-learning approach for robust rank learning. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR 2008) (2008)
Chakrabarti, S., Khanna, R., Sawant, U., Bhattacharyya, C.: Structured learning for non-smooth ranking losses. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 88–96 (2008)
Chapelle, O., Wu, M.: Gradient descent optimization of smoothed information retrieval metrics. Information Retrieval Journal. Special Issue on Learning to Rank 13(3), doi:10.1007/s10791-009-9110-3 (2010)
Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: Advances in Neural Information Processing Systems 10 (NIPS 1997), vol. 10, pp. 243–270 (1998)
Cortes, C., Mohri, M., et al.: Magnitude-preserving ranking algorithms. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 169–176 (2007)
Cossock, D., Zhang, T.: Subset ranking using regression. In: Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006), pp. 605–619 (2006)
Crammer, K., Singer, Y.: Pranking with ranking. In: Advances in Neural Information Processing Systems 14 (NIPS 2001), pp. 641–647 (2002)
Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2003)
Fuhr, N.: Optimum polynomial retrieval functions based on the probability ranking principle. ACM Transactions on Information Systems 7(3), 183–204 (1989)
Gey, F.C.: Inferring probability of relevance using the method of logistic regression. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1994), pp. 222–231 (1994)
Herbrich, R., Obermayer, K., Graepel, T.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers, pp. 115–132 (2000)
Huang, J., Frey, B.: Structured ranking learning using cumulative distribution networks. In: Advances in Neural Information Processing Systems 21 (NIPS 2008) (2009)
Li, P., Burges, C., Wu, Q.: McRank: learning to rank using multiple classification and gradient boosting. In: Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 845–852 (2008)
Nallapati, R.: Discriminative models for information retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2004), pp. 64–71 (2004)
Qin, T., Liu, T.Y., Lai, W., Zhang, X.D., Wang, D.S., Li, H.: Ranking with multiple hyperplanes. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 279–286 (2007)
Qin, T., Liu, T.Y., Li, H.: A general approximation framework for direct optimization of information retrieval measures. Information Retrieval 13(4), 375–397 (2009)
Qin, T., Zhang, X.D., Tsai, M.F., Wang, D.S., Liu, T.Y., Li, H.: Query-level loss functions for information retrieval. Information Processing and Management 44(2), 838–855 (2008)
Rennie, J.D.M., Srebro, N.: Loss functions for preference levels: regression with discrete ordered labels. In: IJCAI 2005 Multidisciplinary Workshop on Advances in Preference Handling. ACM, New York (2005)
Rigutini, L., Papini, T., Maggini, M., Scarselli, F.: Learning to rank by a neural-based sorting algorithm. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR 2008) (2008)
Rudin, C.: Ranking with a p-norm push. In: Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006), pp. 589–604 (2006)
Shashua, A., Levin, A.: Ranking with large margin principles: two approaches. In: Advances in Neural Information Processing Systems 15 (NIPS 2002), pp. 937–944 (2003)
Sun, Z., Qin, T., Tao, Q., Wang, J.: Robust sparse rank learning for non-smooth ranking measures. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), pp. 259–266 (2009)
Talyor, M., Guiver, J., et al.: Softrank: optimising non-smooth rank metrics. In: Proceedings of the 1st International Conference on Web Search and Web Data Mining (WSDM 2008), pp. 77–86 (2008)
Tsai, M.F., Liu, T.Y., Qin, T., Chen, H.H., Ma, W.Y.: Frank: a ranking method with fidelity loss. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 383–390 (2007)
Usunier, N., Buffoni, D., Gallinari, P.: Ranking with ordered weighted pairwise classification. In: Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 1057–1064 (2009)
Veloso, A., Almeida, H.M., Gonçalves, M., Meira, W. Jr.: Learning to rank at query-time using association rules. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 267–274 (2008)
Volkovs, M.N., Zemel, R.S.: Boltzrank: learning to maximize expected ranking gain. In: Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 1089–1096 (2009)
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)
Xu, J., Li, H.: Adarank: a boosting algorithm for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 391–398 (2007)
Xu, J., Liu, T.Y., Lu, M., Li, H., Ma, W.Y.: Directly optimizing IR evaluation measures in learning to rank. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 107–114 (2008)
Yeh, J.Y., Lin, J.Y., et al.: Learning to rank for information retrieval using genetic programming. In: SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (LR4IR 2007) (2007)
Yue, Y., Finley, T., Radlinski, F., Joachims, T.: A support vector method for optimizing average precision. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 271–278 (2007)
Zheng, Z., Chen, K., Sun, G., Zha, H.: A regression framework for learning ranking functions using relative relevance judgments. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 287–294 (2007)
Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K., Sun, G.: A general boosting method and its application to learning ranking functions for web search. In: Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 1697–1704 (2008)
Zoeter, O., Taylor, M., Snelson, E., Guiver, J., Craswell, N., Szummer, M.: A decision theoretic framework for ranking using implicit feedback. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR 2008) (2008)
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). Summary. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-14267-3_19
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)