Selection Prediction Models Considering Context Effects

This study proposes a method for processing unordered datasets using deep learning techniques and introduces a model capable of simultaneously predicting three context effects and selections. “Context effects” refer to dramatic changes in the selection situation caused by the introduction of new opt...

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Vydané v:Journal of advanced computational intelligence and intelligent informatics Ročník 29; číslo 4; s. 734 - 742
Hlavní autori: Zhang, Wenhao, Hasuike, Takashi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tokyo Fuji Technology Press Co. Ltd 20.07.2025
Predmet:
Artificial intelligence
Consumer behavior
Consumers
Context
Decision making
Hierarchies
Impact analysis
Informatics
Machine learning
Neural networks
Prediction models
Preferences
Task complexity
ISSN:1343-0130, 1883-8014
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Popis
Shrnutí:This study proposes a method for processing unordered datasets using deep learning techniques and introduces a model capable of simultaneously predicting three context effects and selections. “Context effects” refer to dramatic changes in the selection situation caused by the introduction of new options into a set of choices. Five experiments were conducted using different product-selection data. Each experiment focused on predicting context effects, selections, predictions under different context effects, predictions for different types of products, and predictions that considered context effects. The results demonstrated that the proposed model is suitable for classification prediction tasks in complex situations. The more complex the model and the larger the amount of data, the better the results. This study extends the application of neural networks to multi-attribute decision-making problems and contributes to the selection of decision-making models. It also improves the prediction accuracy and analyzes the impact of context effects on choices.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p0734