Affective Recommendation System for Tourists by Using Emotion Generating Calculations
April 09, 2018 Β· Declared Dead Β· π International Workshop on Computational Intelligence and Applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Takumi Ichimura, Issei Tachibana
arXiv ID
1804.04946
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.IR
Citations
2
Venue
International Workshop on Computational Intelligence and Applications
Last Checked
4 months ago
Abstract
An emotion orientated intelligent interface consists of Emotion Generating Calculations (EGC) and Mental State Transition Network (MSTN). We have developed the Android EGC application software which the agent works to evaluate the feelings in the conversation. In this paper, we develop the tourist information system which can estimate the user's feelings at the sightseeing spot. The system can recommend the sightseeing spot and the local food corresponded to the user's feeling. The system calculates the recommendation list by the estimate function which consists of Google search results, the important degree of a term at the sightseeing website, and the the aroused emotion by EGC. In order to show the effectiveness, this paper describes the experimental results for some situations during Hiroshima sightseeing.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted