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Predicting user’s preferences using neural networks and psychology models

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Abstract

In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.

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Acknowledgments

The paper is supported by the Slovak Scientific Grant Agency VEGA, Grant No. 1/0142/15.

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Authors and Affiliations

  1. Institute of Computer Science, P. J. Šafárik University in Košice, Jesenná 5, 04001, Košice, Slovakia

    Miron Kuzma & Gabriela Andrejková

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  1. Miron Kuzma
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  2. Gabriela Andrejková
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Correspondence to Miron Kuzma.

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Kuzma, M., Andrejková, G. Predicting user’s preferences using neural networks and psychology models. Appl Intell 44, 526–538 (2016). https://doi.org/10.1007/s10489-015-0717-3

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  • DOI: https://doi.org/10.1007/s10489-015-0717-3

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