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Erschienen in: Soft Computing 3/2013

01.03.2013 | Original Paper

Supervised rank aggregation based on query similarity for document retrieval

verfasst von: Yang Wang, Yalou Huang, Xiaodong Pang, Min Lu, Maoqiang Xie, Jie Liu

Erschienen in: Soft Computing | Ausgabe 3/2013

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Abstract

This paper is concerned with supervised rank aggregation, which aims to improve the ranking performance by combining the outputs from multiple rankers. However, there are two main shortcomings in previous rank aggregation approaches. First, the learned weights for base rankers do not distinguish the differences among queries. This is suboptimal since queries vary significantly in terms of ranking. Besides, most current aggregation functions do not directly optimize the evaluation measures in ranking. In this paper, the differences among queries are taken into consideration, and a supervised rank aggregation function is proposed. This aggregation function is directly optimizing the evaluation measure NDCG, referred to as RankAgg.NDCG, We prove that RankAgg.NDCG can achieve better NDCG performance than the linear combination of the base rankers. Experimental results performed on benchmark datasets show our approach outperforms a number of baseline approaches.

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Literatur
Zurück zum Zitat Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston
Zurück zum Zitat Bian J, Li X, Li F et al (2010a) Ranking specialization for web search: a divide-and-conquer approach by using Topical RankSVM. In: Proceedings of the world wide web conference, Raleigh, pp 131–140 Bian J, Li X, Li F et al (2010a) Ranking specialization for web search: a divide-and-conquer approach by using Topical RankSVM. In: Proceedings of the world wide web conference, Raleigh, pp 131–140
Zurück zum Zitat Bian J, Liu TY, Qin T, et al (2010b) Ranking with query-dependent loss for web search. In: Proceedings of the 3rd WSDM conference. New York, pp 141–150 Bian J, Liu TY, Qin T, et al (2010b) Ranking with query-dependent loss for web search. In: Proceedings of the 3rd WSDM conference. New York, pp 141–150
Zurück zum Zitat Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on machine learning, pp 89–96 Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on machine learning, pp 89–96
Zurück zum Zitat Cao Y, Xu J, Liu T, Li H, Huang Y, Hon HW (2006) Adapting Ranking SVM to document retrieval. In: Proceedings of the 29th ACM SIGIR conference, Seattle, pp 186–193 Cao Y, Xu J, Liu T, Li H, Huang Y, Hon HW (2006) Adapting Ranking SVM to document retrieval. In: Proceedings of the 29th ACM SIGIR conference, Seattle, pp 186–193
Zurück zum Zitat Cao Z, Qin T, Liu T, Tsai M, Li H (2007) Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on machine learning Cao Z, Qin T, Liu T, Tsai M, Li H (2007) Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on machine learning
Zurück zum Zitat Crammer K, Singer Y (2001) PRanking with ranking. In: Proceedings of the 14th conference on neural information processing systems, Vancouver, pp 641–647 Crammer K, Singer Y (2001) PRanking with ranking. In: Proceedings of the 14th conference on neural information processing systems, Vancouver, pp 641–647
Zurück zum Zitat Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969MathSciNet Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969MathSciNet
Zurück zum Zitat Geng XB, Liu TY, Qin T, Arnold A, Li H, Shum HY (2008) Query dependent ranking using K-nearest neighbor, In: Proceedings of the 31st ACM SIGIR conference, pp 115–122 Geng XB, Liu TY, Qin T, Arnold A, Li H, Shum HY (2008) Query dependent ranking using K-nearest neighbor, In: Proceedings of the 31st ACM SIGIR conference, pp 115–122
Zurück zum Zitat Jarvelin K, Kekalainen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Info Syst 20(4):422–446CrossRef Jarvelin K, Kekalainen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Info Syst 20(4):422–446CrossRef
Zurück zum Zitat Joachims T (2002) Optimizing search engines using click-through data. In: Proceedings of the 8th ACM SIGKDD conference, New York, pp 133–142 Joachims T (2002) Optimizing search engines using click-through data. In: Proceedings of the 8th ACM SIGKDD conference, New York, pp 133–142
Zurück zum Zitat Liu YT, Liu TY, Qin T, Ma ZM, Li H (2007) Supervised rank aggregation. In: Proceedings of the world wide web Conference. Alberta, pp 481–490 Liu YT, Liu TY, Qin T, Ma ZM, Li H (2007) Supervised rank aggregation. In: Proceedings of the world wide web Conference. Alberta, pp 481–490
Zurück zum Zitat Peng J, Macdonald C, Ounis I (2010) Learning to select a ranking function. In: Proceedings of the 32nd ECIR conference Peng J, Macdonald C, Ounis I (2010) Learning to select a ranking function. In: Proceedings of the 32nd ECIR conference
Zurück zum Zitat Ponte J, Croft WB (1998) A language model approach to information retrieval. In: Proceedings of the 21st ACM SIGIR conference, pp 275–281 Ponte J, Croft WB (1998) A language model approach to information retrieval. In: Proceedings of the 21st ACM SIGIR conference, pp 275–281
Zurück zum Zitat Qin T, Zhang XD, Tsai MF, Wang DS, Liu TY, Li H (2008) Query-level loss functions for information retrieval. J Info Process Manag Qin T, Zhang XD, Tsai MF, Wang DS, Liu TY, Li H (2008) Query-level loss functions for information retrieval. J Info Process Manag
Zurück zum Zitat Qin T, Liu TY, Lai W et al (2007) Ranking with multiple hyperplanes. In: Proceedings of the 30st ACM SIGIR conference, The Netherlands, pp 279–286 Qin T, Liu TY, Lai W et al (2007) Ranking with multiple hyperplanes. In: Proceedings of the 30st ACM SIGIR conference, The Netherlands, pp 279–286
Zurück zum Zitat Qin T, Liu T, Li H (2009) A general approximation framework for direct optimization of information retrieval measures. J Info Retr Qin T, Liu T, Li H (2009) A general approximation framework for direct optimization of information retrieval measures. J Info Retr
Zurück zum Zitat Qin T, Liu T, Xu J, Li H (2009) LETOR: a benchmark collection for research on learning to rank for information retrieval. J Info Retr Qin T, Liu T, Xu J, Li H (2009) LETOR: a benchmark collection for research on learning to rank for information retrieval. J Info Retr
Zurück zum Zitat Robertson SE, Walker S, Hancock-Beaulieu M, Gatford M (1994) Okapi in TREC3, In: Proceedings of text retrieval conference, Gaithersburg, pp 109–126 Robertson SE, Walker S, Hancock-Beaulieu M, Gatford M (1994) Okapi in TREC3, In: Proceedings of text retrieval conference, Gaithersburg, pp 109–126
Zurück zum Zitat Shashua A, Levin A (2003) Ranking with large margin principle: two approaches. In: Proceedings of the 15th conference on neural information processing systems, pp 937–944 Shashua A, Levin A (2003) Ranking with large margin principle: two approaches. In: Proceedings of the 15th conference on neural information processing systems, pp 937–944
Zurück zum Zitat Xia F, Liu T, Wang J et al (2008) Listwise approach to learning to rank—theory and algorithm. In: Proceedings of the 25th ICML conference, Finland, pp 1192–1199 Xia F, Liu T, Wang J et al (2008) Listwise approach to learning to rank—theory and algorithm. In: Proceedings of the 25th ICML conference, Finland, pp 1192–1199
Zurück zum Zitat Xu J, Li H (2007) AdaRank: a boosting algorithm for information retrieval. In: Proceedings of the 30th ACM SIGIR conference, pp 391–398 Xu J, Li H (2007) AdaRank: a boosting algorithm for information retrieval. In: Proceedings of the 30th ACM SIGIR conference, pp 391–398
Zurück zum Zitat Yue Y, Finley T, Radlinski F et al (2007) A support vector method for optimizing average precision. In: Proceedings of the 30th ACM SIGIR conference, The Netherlands Yue Y, Finley T, Radlinski F et al (2007) A support vector method for optimizing average precision. In: Proceedings of the 30th ACM SIGIR conference, The Netherlands
Metadaten
Titel
Supervised rank aggregation based on query similarity for document retrieval
verfasst von
Yang Wang
Yalou Huang
Xiaodong Pang
Min Lu
Maoqiang Xie
Jie Liu
Publikationsdatum
01.03.2013
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 3/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0917-2

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