Predicting user’s preferences using neural networks and psychology models

Kuzma, Miron; Andrejková, Gabriela

doi:10.1007/s10489-015-0717-3

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…As it may appear as a flaw, it is actually an advantage, because ANNs don't require physical pre-information before modeling a system, and ANNs are used when there is a need for complex answers and algorithms and possible relations between data are unknown. There is a wide range of functionalities provided by these algorithms what makes them promising for use in predicting in psychology, including: mental health, behavior, emotions, and personality traits [17][18][19][20][21]. ANNs approaches explicitly concentrate on statistical learning of nonlinear functions from multidimensional data sets to make further generalized predictions about data vectors not seen during training.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for predicting social comparison effects among female Instagram users

Jabłońska

Zajdel

2020

PLoS ONE

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Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, selfobjectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18-49 who were Instagram users voluntarily participated, completing a questionnaire. The results suggest associations between the analyzed psychological data and social comparison types. Then, artificial neural networks models were implemented to predict the type of such comparison (positive, negative, equal) based on the aforementioned psychological traits. The models were able to properly predict between 71% and 82% of cases. As human behavior analysis has been a subject of study in various fields of science, this paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology.

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Section: Introductionmentioning

confidence: 99%

Artificial neural networks for predicting social comparison effects among female Instagram users

Jabłońska

Zajdel

2020

PLoS ONE

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“…(2) self-efficacy [33]; (3) self-esteem [34]; (4) extraversion; (5) emotional stability; (6) responsibility; (7) kindness; (8) open-mindedness to experience; (9) flourishing; engagement, [34] which is split into four variables: (10) vigour, (11) dedication, (12) absorption, and (13) total engagement; (14) satisfaction with life [35][36][37]; emotional intelligence [38] also divided into four items: (15) perception; (16) comprehension; (17) regulation; (18) total emotional intelligence; and finally, personal and organisational quality [39], which involves (19) emotional vitality, (20) organisational stress, (21) emotional stress, (22) physical stress, (23) abandonment, (24) health risk and (25) total.…”

Section: Methods Participants and Proceduresmentioning

confidence: 99%

“…Although psychology has taken longer than other fields to adopt machine learning models for the analysis of experimental results, an increasing number of research works have shown the effectiveness of these models and their benefits in complementing traditional statistical techniques [1][2][3][4][5][6][7][8] As a result, machine learning methods are becoming a valuable tool in differential statistics, supporting the development of more accurate forecasting models through the generalisation and evaluation of new data [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People

Pegalajar

Ruiz

Pérez-Moreiras³

et al. 2023

BDCC

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The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person’s efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multi-layer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow.

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Developing a closed captioning quality assessment system using a multi-label classifier with active learning from deaf and hard of hearing viewers

2023

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Predicting user’s preferences using neural networks and psychology models

Cited by 10 publications

References 12 publications

Artificial neural networks for predicting social comparison effects among female Instagram users

Artificial neural networks for predicting social comparison effects among female Instagram users

An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People

Developing a closed captioning quality assessment system using a multi-label classifier with active learning from deaf and hard of hearing viewers

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