This study aimed to propose a novel method for designing a product recommendation virtual agent (PRVA) that can keep users motivated to interact with the agent. In prior papers, many methods of keeping users motivated postulated real-time and multi-modal interactions. The proposed novel method can be used in one-direction interaction. We defined the notion of the "hidden vector," that is, information that is not mentioned by a PRVA and that the user can suppose spontaneously. We conducted an experiment to verify the hypothesis that PRVAs having a hidden vector are more effective than other PRVAs. As a result, it was shown that PRVAs having a hidden vector were perceived as being more persuasive than other PRVAs and strongly motivated the users to use the PRVAs. From these results, the proposed method was shown to be effective.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194823 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2021.627148 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.
Sci Rep
January 2025
Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets.
View Article and Find Full Text PDFModeling of a Novel T-Core Sensor with an Air Gap for Applications in Eddy Current Nondestructive Evaluation.
Sensors (Basel)
December 2024
Department of Electrical and Automation, Shanghai Maritime University, Shanghai 201306, China.
Multi-layer conductive structures, especially those with features like bolt holes, are vulnerable to hidden corrosion and cracking, posing a serious threat to equipment integrity. Early defect detection is vital for implementing effective maintenance strategies. However, the subtle signals produced by these defects necessitate highly sensitive non-destructive testing (NDT) techniques.
View Article and Find Full Text PDFGenerative dynamical models for classification of rsfMRI data.
Netw Neurosci
December 2024
Department of Psychology, Stanford University, Stanford, CA, USA.
The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data.
View Article and Find Full Text PDFReliability of wing morphometrics for species identification of human-biting black flies (Diptera: Simuliidae) in Thailand.
Parasit Vectors
December 2024
Parasitology and Entomology Research Cluster (PERC), Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Background: Fast and reliable species identification of black flies is essential for research proposes and effective vector control. Besides traditional identification based on morphology, which is usually supplemented with molecular methods, geometric morphometrics (GM) has emerged as a promising tool for identification. Despite its potential, no specific GM techniques have been established for the identification of black fly species.
View Article and Find Full Text PDFOptical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!