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.
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.
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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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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