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Computational Mathematics and Variational Analysis pp 279–297Cite as

Unsupervised Stochastic Learning for User Profiles

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 159))

Abstract

An unsupervised learning method for user profiles is examined. A user profile is considered the set of all the queries a user issues against an information or a database system. The mechanism of the Markovian model is employed where probabilistic locality translates to semantic locality in ways that facilitate a hierarchical clustering with optimal properties.

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Notes

  1. 1.

    A Markovian process of a single chain, having a limiting matrix with identical rows, duodesmic if the process consists of two chains, tridesmic of three chains, etc.

  2. 2.

    Normalised with respect to the l 1 norm, the sum of the absolute coordinates equals 1. All probability vectors are normalised with respect to l 1.

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Author information

Authors and Affiliations

  1. Hellenic Military Academy, Vari Attikis, Greece

    Nikolaos K. Papadakis

Authors
  1. Nikolaos K. Papadakis
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Correspondence to Nikolaos K. Papadakis .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Hellenic Military Academy, Vari Attikis, Greece

    Nicholas J. Daras

  2. Department of Mathematics, National Technical University of Athens, Athens, Greece

    Themistocles M. Rassias

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Papadakis, N.K. (2020). Unsupervised Stochastic Learning for User Profiles. In: Daras, N., Rassias, T. (eds) Computational Mathematics and Variational Analysis. Springer Optimization and Its Applications, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-44625-3_16

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