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Recent Developments in Fuzzy Logic and Fuzzy Sets pp 81–103Cite as

Semi-supervised Learning to Rank with Nonlinear Preference Model

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 391))

Abstract

The problem of learning to rank in static and dynamic formulation is considered, wherein expert or user’s preference function model is used as a ranking function. In order to reduce dimension of the problem, ranking learning problem on clusters in feature space is stated, while aggregated training dataset consist of estimates of clusters centers and average rank of the items inside each cluster. The problem of both linear and nonlinear models of preference functions building is considered, and in the latter case the method of kernel-based learning in pointwise and pairwise framework. is used. Estimates preference function models are found as solutions of corresponding regularized optimization problems, and a hierarchical regularization scheme is implemented with successive use of a priori estimates of feature weights and estimates of linear model parameters. Within semi-supervised learning concept, unlabeled dataset provides additional regularization for estimated preference model smoothing using the graph data model, considering data geometric structure. Online ranking learning is implemented using training dataset in the form of a sequence of identical items series, described by measured features and relative rank within the series. Recurrent algorithms for estimating the parameters of preference function model as well as recurrent ranking learning algorithm in the space of conjugate variables are developed.

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Author information

Authors and Affiliations

  1. Department of Computer Mathematics and Data Analysis, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, 61002, Ukraine

    L. Lyubchyk & A. Galuza

  2. Department of Economical Cybernetics and Marketing Management, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, 61002, Ukraine

    G. Grinberg

Authors
  1. L. Lyubchyk
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  2. A. Galuza
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  3. G. Grinberg
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Corresponding author

Correspondence to L. Lyubchyk .

Editor information

Editors and Affiliations

  1. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  2. Tokyo Institute of Technology, Kamakura, Japan

    Michio Sugeno

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Lyubchyk, L., Galuza, A., Grinberg, G. (2020). Semi-supervised Learning to Rank with Nonlinear Preference Model. In: Shahbazova, S., Sugeno, M., Kacprzyk, J. (eds) Recent Developments in Fuzzy Logic and Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-030-38893-5_5

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