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Ranking Learning-to-Rank Methods

Djoerd Hiemstra, Niek Tax, Sander Bockting
2017 International Conference on the Theory of Information Retrieval  
Evaluation results of 87 learning-to-rank methods on 20 datasets show that ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning-to-rank methods, listed in increasing order  ...  We present a cross-benchmark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number.  ...  These learning-to-rank datasets offer feature set representations of the to-be-ranked documents instead of the documents themselves.  ... 
dblp:conf/ictir/HiemstraTB17 fatcat:gq22aeg5ave5rbmeyspln2qqu4

Learning to rank

Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects.  ...  Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning. We refer to them as the pairwise approach in this paper.  ...  We would also like to thanks Kai Yi for his help in our Learning to Rank: From Pairwise Approach to Listwise Approach experiments.  ... 
doi:10.1145/1273496.1273513 dblp:conf/icml/CaoQLTL07 fatcat:soldduou45cu5b7n6ni22qtoym

Learning to Rank

Richard Combes, Stefan Magureanu, Alexandre Proutiere, Cyrille Laroche
2015 Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems - SIGMETRICS '15  
Algorithms for learning to rank Web documents, display ads, or other types of items constitute a fundamental component of search engines and more generally of online services.  ...  The main challenge in the design of sequential list selection algorithms stems from the fact that the probabilities with which the user clicks on the various items are unknown and need to be learned.  ...  INTRODUCTION In this paper, we address the problem of learning to rank a set of items based on user feedback.  ... 
doi:10.1145/2745844.2745852 dblp:conf/sigmetrics/CombesMPL15 fatcat:soa44abzivcnfnsx4ymxvbyg44

Learning to Rank [chapter]

2014 Encyclopedia of Social Network Analysis and Mining  
When Online… ¢ When you "go online", many things are beyond your control What is learning to rank?  ...  Reusing Historical Interaction Data for Faster Online Learning to Rank for IR.  ...  Method Today's state-of-the-art ranking models combine the scores produced by many base rankers and compute a combination of them to arrive at a high-quality ranking.  ... 
doi:10.1007/978-1-4614-6170-8_100897 fatcat:6r5nhrdmvbfbnpwmixtscxi5xy

Learning to Rank [chapter]

Hang Li
2017 Encyclopedia of Machine Learning and Data Mining  
I will first explain the problem formulation of learning to rank, and relations between learning to rank and the other learning tasks.  ...  I will then give an introduction to the theoretical work on learning to rank and the applications of learning to rank. Finally, I will show some future directions of research on learning to rank.  ... 
doi:10.1007/978-1-4899-7687-1_893 fatcat:i5ruyn5m2fgz7gxlrckxww3dgm

Learning to Rank Learning Curves [article]

Martin Wistuba, Tejaswini Pedapati
2020 arXiv   pre-print
We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many  ...  In contrast to existing methods, we consider this task as a ranking and transfer learning problem.  ...  This way of obtaining a ranking is referred to as pointwise ranking methods (Liu, 2011) .  ... 
arXiv:2006.03361v1 fatcat:yrhcep6dgnejvd7c3jrleg3gta

Ranking Models and Learning to Rank: A Survey

2015 International Journal of Science and Research (IJSR)  
Learning to rank is flattering progressively more trendy research area in machine learning.  ...  This paper mainly focuses on survey of the ranking models and learning to rank technique for giving the effective and efficient information retrieval.  ...  When user queries, the documents have to be ranked according to the relevance to the query. Various machine learning algorithms are being used to learn the ranking function.  ... 
doi:10.21275/v4i12.nov151889 fatcat:svwiztlpr5e2zb2atqaraj5pxy

Learning to Un-Rank

Asia J. Biega, Azin Ghazimatin, Hakan Ferhatosmanoglu, Krishna P. Gummadi, Gerhard Weikum
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version.  ...  and learned rankings.  ...  We want to learn a ranking function that minimizes a loss measure over these partial training rankings.  ... 
doi:10.1145/3132847.3133040 dblp:conf/cikm/BiegaGFGW17 fatcat:4xh3yoxwnjc5vb5ujbqlhjteza

Online Learning to Rank

Yiwei Chen, Katja Hofmann
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
Online learning to rank holds great promise for learning personalized search result rankings.  ...  It is therefore unclear which type of approach is the most suitable for which online learning to rank problems.  ...  We would like to thank Jun Wang and Emine Yilmaz for supporting this work.  ... 
doi:10.1145/2740908.2742718 dblp:conf/www/ChenH15 fatcat:blhsk3k7jfcivipz4wfclib4vi

Entity Attribute Ranking Using Learning to Rank

Esraa Ali, Annalina Caputo, Séamus Lawless
2017 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval  
We model this problem as a learning to rank approach applied to entity attributes.  ...  The entity label is submitted as a search query to a general search engine and the top ranked documents are used to add context to the ranking process.  ...  That score is passed to a learning to rank algorithm to enhance the ranking results.  ... 
dblp:conf/sigir/AliCL17 fatcat:moxdwgstlbgxnplbmyau6lpdli

Learning to rank tags

Zheng Wang, Jiashi Feng, Changshui Zhang, Shuicheng Yan
2010 Proceedings of the ACM International Conference on Image and Video Retrieval - CIVR '10  
In this paper, we present a novel semi-supervised learning framework to rank image tags, which learns a ranking projection with theoretic guarantee from visual words distribution to the relevant tags distribution  ...  Also as the manual ranking is laborious especially for large scale data collections, we propose an active learning scheme to guide the user ranking process and efficiently obtain the informative tag ranking  ...  A new tag graph has to be learned for ranking the tags of another image.  ... 
doi:10.1145/1816041.1816049 dblp:conf/civr/WangFZY10 fatcat:mrwf4czrhjd2xnje4ypfroupwm

Metric Learning to Rank

Brian McFee, Gert R. G. Lanckriet
2010 International Conference on Machine Learning  
We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking  ...  We study metric learning as a problem of information retrieval.  ...  Algorithm 1 Metric Learning to Rank (MLR).  ... 
dblp:conf/icml/McFeeL10 fatcat:glkhycat6jhhvoy62xgrquricy

Reinforcement Learning to Rank

Maarten de Rijke
2019 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19  
Reinforcement Learning to Rank ABSTRACT: Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users.  ...  Reinforcement Learning to Rank. In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19), February 11-15, 2019, Melbourne, VIC, Australia.  ... 
doi:10.1145/3289600.3291605 dblp:conf/wsdm/Rijke19 fatcat:ai4c2x425vah3jlgsptndbwuby

Learning to rank collections

Jingfang Xu, Xing Li
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
In this paper, we exploit some new features and learn to rank collections with them through SVM and RankingSVM respectively.  ...  Experimental results show that our features are beneficial to collection selection, and the learned ranking functions outperform the classical CORI algorithm.  ...  Ranking To combine the features, we employ RankingSVM and SVM to learn a ranking function respectively, which ranks collections according to queries.  ... 
doi:10.1145/1277741.1277898 dblp:conf/sigir/XuL07a fatcat:unh7xnkvpbhv5dl7os4kartnii

Learning to re-rank

Vidit Jain, Manik Varma
2011 Proceedings of the 20th international conference on World wide web - WWW '11  
Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker.  ...  We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list.  ...  Images are re-ranked based on their likelihood ratio. Observing that discriminative learning can lead to superior results, Schroff et al. [26] first learn a query independent text based re-ranker.  ... 
doi:10.1145/1963405.1963447 dblp:conf/www/JainV11 fatcat:inpgz3ncxjgslcbitt5snxwg3m
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