A stochastic learning-to-rank algorithm and its application to contextual advertising

Proceedings of the 20th International Conference on World Wide Web, WWW 2011

Volumn , Issue , 2011, Pages 377-386

  Karimzadehgan, Maryam ^a   Li, Wei ^b   Zhang, Ruofei ^b   Mao, Jianchang ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

Contextual advertising; IR measures; Learning to rank; NDCG; NDCG annealing; Simplex algorithm; Simulated annealing

Indexed keywords

CONTEXTUAL ADVERTISINGS; LEARNING TO RANK; NDCG; NDCG-ANNEALING; SIMPLEX ALGORITHM;

MARKETING; QUERY LANGUAGES; SIMULATED ANNEALING; STOCHASTIC SYSTEMS; WORLD WIDE WEB;

LEARNING ALGORITHMS;

EID: 84455207062     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1963405.1963460     Document Type: Conference Paper

References (50)

1. H. M. D. Almeida, M. A. Concalves, M. Cristo, and P. Calado. A combined component approach for finding collection-adapted ranking functions based on genetic programming. ACM SIGIR, pages 399-406, 2007.
2. R. Baeza-Yates and B. Ribeiro-Neto. Modern information retrieval. Addison Wesley.
3. A. Bagherjeiran, A. Hatch, and A. Ratnaparkhi. Ranking for the conversion funnel. ACM SIGIR, pages 146-153, 2010.
4. S. Bandyopadhyay, S. K. Pal, and C. A. Murthy. Simulated annealing based pattern classification. Information Sciences, pages (109):165-184, 1998.
5. A. Z. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. SIGIR, pages 559-566, 2007.
6. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. ICML, pages 89-96, 2005.
7. C. J. Burges, R. Ragno, and Q. V. Le. Learning to rank with nonsmooth cost functions. NIPS, pages 193-200, 2006.
8. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon. Adapting ranking svm to document retrieval. ACM SIGIR, pages 186-193, 2006.
9. Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li. Learning to rank: From pairwise approach to listwise approach. ICML, pages 129-136, 2007.
10. D. Chakrabarti, D. Agrawal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. WWW, pages 416-426, 2008.
11. O. Chapelle and M. Wu. Gradient descent optimization of smoothed information retrieval metrics. IR Journal, pages 13(3):201-215, 2010.
12. D. Cossock and T. Zhang. Subset ranking using regression. CoLT, pages 605-619, 2006.
13. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Symposium on Operating System Design and Implementation, pages 137-150, 2004.
14. K. A. Dowsland. Simulated annealing. Modern Heuristic Techniques for Combinatorial Problems, 1993.
15. M. feng Tsai, T.-Y. Liu, T. Qin, H.-H. Chen, and W.-Y. Ma. Frank: A ranking method with fidelity loss. ACM SIGIR, pages 383-390, 2007.
16. A. Foundation. Apache hadoop project. http://hadoop.apache.org/.
17. Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. ICML, pages 170-178, 1998.
18. R. Herbrich, T. Graepel, and K. Obermayer. Support vector learning for ordinal regression. Artificial Neural Networks, pages 97-102, 1999.
19. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, pages 115-132, 2000.
20. C. Huang and R. Nevatia. High performance object detection by collaborative learning of joint ranking of granules features. CVPR, pages 41-48, 2010.
21. K. Jarvelin and J. Kekalainen. Ir evaluation methods for retrieving highly relevant documents. ACM SIGIR, pages 41-48, 2000.
22. T. Joachims. Optimizing search engines using clickthrough data. ACM KDD, pages 133-142, 2002.
23. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, pages 671-680, 1983.
24. A. Lacerda, M. Cristo, M. Goncalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. ACM SIGIR, pages 549-556, 2006.
25. P. Li, C. J. Burges, and Q. Wu. Mcrank: Learning to rank using multiple classification and gradient boosting. NIPS, 2007.
26. T. Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, pages 3(3):225-331, 2009.
27. T.-Y. Liu, J. Xu, T. Qin, W. Xiong, and H. Li. Letor: Benchmark dataset for research on learning to rank for information retrieval. SIGIR-LTR, 2007.
28. A. V. Lukashin and R. Fuchs. Analysis of temporal gene expression profiles: Clustering by simulated annealing and determining the optimal number of clusters. Bioinformatics, pages 405-414, 2000.
29. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equation of state calculation by fast computing machines. J. Of Chem. Phys., pages 1087-1091, 1953.
30. V. Murdock, M. Ciaramita, and V. Plachouras. A noisy-channel approach to contextual advertising. ADKDD, pages 21-27, 2007.
31. R. Nallapati. Discriminative models for information retrieval. ACM SIGIR, pages 64-71, 2004.
32. J. A. Nelder and R. Mead. A simplex method for function minimization. Computer Journal, pages (7):308-313, 1965.
33. Z. Nie, Y. Zhang, J.-R. Wen, and W.-Y. Ma. Object-level ranking: Bringing order to web objects. WWW, pages 567-574, 2005.
34. J. Peng, C. Macdonald, and I. Ounis. Learning to select a ranking function. ECIR, pages 111-126, 2010.
35. J. Ponte and W. Croft. A language modeling approach to information retrieval. ACM SIGIR, pages 275-281, 1998.
36. T. Qin, T.-Y. Liu, M. feng Tsai, X.-D. Zhang, and H. Li. Learning to search web pages with query-level loss functions. Technical Report, 2006.
37. A. Ratnaparkhi. A hidden class page-ad probability model for contextual advertising. WWW Workshop, 2008.
38. M. Regelson and D. Fian. Predicting click-through rate using keyword clusters. ACM EC, 2006.
39. B. Ribeiro-Neto, M. Cristo, P. Golgher, and E. S. D. Moura. Impedance coupling in content-targeted advertising. ACM SIGIR, pages 496-503, 2005.
40. M. Richardson, E. Dominowska, and R. Rango. Predicting clicks: Estimating the click-through rate for new ads. WWW, pages 521-530, 2007.
41. S. Robertson and K. S. Jones. Relevance weighting of search terms. Journal of the American Society for Information Science, pages 27:129-146, 1976.
42. S. Robertson and S. Walker. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. ACM SIGIR, pages 232-241, 1994.
43. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, pages 24(5):513-523, 1988.
44. M. Taylor, J. Guiver, S. Robertson, and T. Minka. Softrank: optimizing non-smooth rank metrics. ACM WSDM, pages 77-86, 2008.
45. H. Valizadegan, R. Jin, R. Zhang, and J. Mao. Learning to rank by optimizing ndcg measure. NIPS, 2009.
46. M. N. Volkovs and R. S. Zemel. Boltzrank: learning to maximize expected ranking gain. ICML, pages 1089-1096, 2009.
47. J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. ACM SIGIR, pages 391-398, 2007.
48. W. Yang, L. Rueda, and A. Ngom. A simulated annealing approach to find the optimal parameters for fuzzy clustering microarray data. IEEE International Conference on The Chilean Computer Science Society, 2005.
49. J.-Y. Yeh, J.-Y. Lin, H.-R. Ke, and W.-P. Yang. Learning to rank for information retrieval using genetic programming. SIGIR-LTR Workshop, 2007.
50. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. ACM SIGIR, pages 271-278, 2007.