Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering
Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/6/1997 |
| _version_ | 1797541627454029824 |
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| author | Tae-Yeun Kim Hoon Ko Sung-Hwan Kim Ho-Da Kim |
| author_facet | Tae-Yeun Kim Hoon Ko Sung-Hwan Kim Ho-Da Kim |
| author_sort | Tae-Yeun Kim |
| collection | DOAJ |
| description | Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application. |
| first_indexed | 2024-03-10T13:19:35Z |
| format | Article |
| id | doaj.art-b6a7d493625f4d5497dccf814013899b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:19:35Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b6a7d493625f4d5497dccf814013899b2023-11-21T10:10:52ZengMDPI AGSensors1424-82202021-03-01216199710.3390/s21061997Modeling of Recommendation System Based on Emotional Information and Collaborative FilteringTae-Yeun Kim0Hoon Ko1Sung-Hwan Kim2Ho-Da Kim3National Program of Excellence in Software Center, Chosun University, Gwangju 61452, KoreaIT Research Institute, Chosun University, Gwangju 61452, KoreaNational Program of Excellence in Software Center, Chosun University, Gwangju 61452, KoreaNational Program of Excellence in Software Center, Chosun University, Gwangju 61452, KoreaEmotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.https://www.mdpi.com/1424-8220/21/6/1997collaborative filteringemotion recognitionsupport vector machine algorithmspeech emotion information |
| spellingShingle | Tae-Yeun Kim Hoon Ko Sung-Hwan Kim Ho-Da Kim Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering Sensors collaborative filtering emotion recognition support vector machine algorithm speech emotion information |
| title | Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering |
| title_full | Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering |
| title_fullStr | Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering |
| title_full_unstemmed | Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering |
| title_short | Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering |
| title_sort | modeling of recommendation system based on emotional information and collaborative filtering |
| topic | collaborative filtering emotion recognition support vector machine algorithm speech emotion information |
| url | https://www.mdpi.com/1424-8220/21/6/1997 |
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