Proceedings ArticleDOI
Learning to rank: from pairwise approach to listwise approach
Zhe Cao,Tao Qin,Tie-Yan Liu,Ming-Feng Tsai,Hang Li +4 more
- pp 5-5
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TLDR
It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.Abstract:
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. 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. Although the pairwise approach offers advantages, it ignores the fact that ranking is a prediction task on list of objects. The paper postulates that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning. The paper proposes a new probabilistic method for the approach. Specifically it introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning. Neural Network and Gradient Descent are then employed as model and algorithm in the learning method. Experimental results on information retrieval show that the proposed listwise approach performs better than the pairwise approach.read more
Citations
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Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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Learning to Rank for Information Retrieval
TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.
Proceedings ArticleDOI
Relative attributes
Devi Parikh,Kristen Grauman +1 more
TL;DR: This work proposes a generative model over the joint space of attribute ranking outputs, and proposes a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, ‘bears are furrier than giraffes’).
Proceedings ArticleDOI
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks
TL;DR: This paper presents a convolutional neural network architecture for reranking pairs of short texts, where the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data are learned.
Proceedings ArticleDOI
Learning structural SVMs with latent variables
Chun-Nam Yu,Thorsten Joachims +1 more
TL;DR: A large-margin formulation and algorithm for structured output prediction that allows the use of latent variables and the generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.
References
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Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Proceedings ArticleDOI
Optimizing search engines using clickthrough data
TL;DR: The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.
Proceedings ArticleDOI
Learning to rank using gradient descent
Chris J.C. Burges,Tal Shaked,Erin L. Renshaw,Ari Lazier,Matt Deeds,Nicole A. Hamilton,Greg Hullender +6 more
TL;DR: RankNet is introduced, an implementation of these ideas using a neural network to model the underlying ranking function, and test results on toy data and on data from a commercial internet search engine are presented.
Book
Learning to Rank for Information Retrieval
TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.