Detecting Memory-Based Interaction Obstacles with a Recurrent Neural Model of User Behavior
| Autor: | Mazen Salous, Tanja Schultz, Felix Putze |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Cognitive user
Secondary task Computer science business.industry 05 social sciences Human memory Machine learning computer.software_genre Test (assessment) 03 medical and health sciences 0302 clinical medicine Recurrent neural network System use Obstacle 0501 psychology and cognitive sciences Artificial intelligence business Baseline (configuration management) computer 050107 human factors 030217 neurology & neurosurgery |
| Zdroj: | IUI |
| DOI: | 10.1145/3172944.3173006 |
| Popis: | A memory-based interaction obstacle is a condition which impedes human memory during Human-Computer Interaction, for example a memory-loading secondary task. In this paper, we present an approach to detect the presence of such memory-based interaction obstacles from logged user behavior during system use. For this purpose, we use a recurrent neural network which models the resulting temporal sequences. To acquire a sufficient number of training episodes, we employ a cognitive user simulation. We evaluate the approach with data from a user test and on which we outperform a non-sequential baseline by up to 42% relative. |
| Databáze: | OpenAIRE |
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