A Short Introduction to Learning to Rank

By Hang Li. IEICE Trans. Inf. Syst., 2011

371 citations recorded by the source corpus.

Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made[1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d74a1419d75e8743eb7e3da2bb425340c7753342

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, methodology): “We then employ RankNet [16], a neural networkbased pairwise learning-to-rank (L2R) algorithm [17], [18], to learn an opinion-unaware BIQA (OU-BIQA, meaning that ar X iv :1 90 4.”
Semantic Matching in Search (2014, methodology): “The machine learning techniques concerned with in this survey include statistical language model [204], statistical machine translation [99], learning to rank [128, 115, 116], graphical model [24], topic model [25], matrix factorization [103], kernel methods [158], sparse methods 3, and deep learnin”
Towards Conversational Recommender Systems (2016, methodology): “AP@k is a widely used, precision-oriented metric [15] for capturing accuracy in the top k (we set k = 10).”
An Information Retrieval Approach to Short Text Conversation (2014, background): “Learning to rank and semantic matching are considered state-of-the-art techniques for search (Liu, 2009; Li, 2011a,b; Li and Xu, 2014).”
GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017, background): “The basic idea of GOLabeler is to integrate different types of sequence-based evidence in the framework of “learning to rank” (LTR) [17].”
Online Learning: A Comprehensive Survey (2018, background): “Learning to rank is an important family of machine learning techniques for information retrieval (Trotman, 2005; Cao et al., 2007; Hang, 2011; Zoghi et al., 2017; Shah et al., 2017).”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, background): “Similarly a pairwise ranking approach can be formulated as pairwise classification or pairwise regression [129-136], for instance where a model is trained on restraints derived from the fact that acceptable docking poses are superior to incorrect poses, medium are superior to acceptable and incorrec”
Sort Story: Sorting Jumbled Images and Captions into Stories (2016, background): “Similar to learning to rank approaches (Hang, 2011), we develop pairwise scoring models that given a pair of elements (i, j), learn to assign a score: S([[σi < σj ]] | i, j) indicating whether element i should be placed before element j in the permutation σ.”
Learning to Rank for Information Retrieval and Natural Language Processing (2011, neutral): “Learning to rank is a sub-area of machine learning, studying methodologies and theories for automatically constructing a model from data for a ranking problem [1, 2, 3].”