Supervised Ordinal Prediction Algorithm

A Supervised Ordinal Prediction Algorithm is a supervised prediction algorithm that can be applied by a supervised ordinal prediction system (to solve a supervised ordinal prediction task for training datasets with ordinal response variable).

• AKA: Learning-to-Rank (LTR) Method, Ordinal Regression Method.
• Context:
  • It can range from being a Pointwise Ranking Algorithm to being a Pairwise Ranking Algorithm to being a Listwise Ranking Algorithm.
  • It can range from being an Offline Supervised Ordinal Prediction Algorithm to being an Online Supervised Ordinal Prediction Algorithm.
  • …
• Example(s):
  • a Pairwise Ranking Algorithm, such as a BPR algorithm.
  • a RankSVM Algorithm.
  • an Ordinal Logistic Regression.
  • a Low-Rank Matrix Factorization Algorithm.
  • …
• Counter-Example(s):
  • a Supervised Regression Algorithm.
  • a Supervised Classification Algorithm.
• See: Ordinal Variable, Ordered Logit, Personalized Recommendation Method.

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References

2022

• (Li, 2022) ⇒ Hang Li. (2022). “Learning to Rank for Information Retrieval and Natural Language Processing.” Springer Nature. ISBN:9783031021558
  • OVERVIEW: ... Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed.

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Ordinal_regression Retrieved:2015-1-10.
  • In statistics, ordinal regression is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. The two most common types of ordinal regression models are ordered logit, which applies to data that meet the proportional odds assumption, and ordered probit.

2013

• (Hofmann et al., 2013) ⇒ Katja Hofmann, Shimon Whiteson, and Maarten de Rijke. (2013). “Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval.” In: Information Retrieval, 16(1).

2009

• (Liu, 2009) ⇒ Tie-Yan Liu. (2009). “Learning to Rank for Information Retrieval.” Foundations and Trends ® in Information Retrieval, 3(3).
  • QUOTE: ... Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches.

2005

• (Liu & Agresti, 2005) ⇒ Ivy Liu, and Alan Agresti. (2005). “The Analysis of Ordered Categorical Data: An Overview and a Survey of Recent Developments.” Test 14, no. 1
  • ABSTRACT: This article review methodologies used for analyzing ordered categorical (ordinal) response variables. We begin by surveying models for data with a single ordinal response variable. We also survey recently proposed strategies for modeling ordinal response variables when the data have some type of clustering or when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. Primary models in that case includemarginal models andcluster-specific (conditional) models for which effects apply conditionally at the cluster level. Related discussion refers to multi-level and transitional models. The main emphasis is on maximum likelihood inference, although we indicate certain models (e.g., marginal models, multi-level models) for which this can be computationally difficult. The Bayesian approach has also received considerable attention for categorical data in the past decade, and we survey recent Bayesian approaches to modeling ordinal response variables. Alternative, non-model-based, approaches are also available for certain types of inference.

1999

• (Herbrich et al., 1999) ⇒ Ralf Herbrich, Thore Graepel, and Klaus Obermayer. (1999). “Support Vector Learning for Ordinal Regression.” In: Proceedings of the Ninth International Conference on Artificial Neural Networks.
  • QUOTE: We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. ...

    ... Problems of ordinal regression arise in many fields, e.g., in information retrieval (Herbrich et al. 1998), in econometric models (Tangian and Gruber 1995), and in classical statistics (McCullagh 1980; Anderson 1984).

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