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Sung-Hwan Kim
National Program of Excellence in Software Center, Chosun University, Gwangju 61452, Korea

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Journal article
Published: 12 March 2021 in Sensors
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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.

ACS Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors 2021, 21, 1997 .

AMA Style

Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim, Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors. 2021; 21 (6):1997.

Chicago/Turabian Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. 2021. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6: 1997.

Journal article
Published: 25 June 2020 in Sustainability
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As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.

ACS Style

Tae-Yeun Kim; Sung Bum Pan; Sung-Hwan Kim. Sentiment Digitization Modeling for Recommendation System. Sustainability 2020, 12, 5191 .

AMA Style

Tae-Yeun Kim, Sung Bum Pan, Sung-Hwan Kim. Sentiment Digitization Modeling for Recommendation System. Sustainability. 2020; 12 (12):5191.

Chicago/Turabian Style

Tae-Yeun Kim; Sung Bum Pan; Sung-Hwan Kim. 2020. "Sentiment Digitization Modeling for Recommendation System." Sustainability 12, no. 12: 5191.

Conference paper
Published: 27 November 2019 in Communications in Computer and Information Science
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Massive Machine-Type-Communication (mMTC) is expected to play a crucial role in 5G networks to enable Internet of Things (IoT). But with deployment of lots of mMTC devices, mobile cellular network will suffer from problems of congestion and large system overhead in both the Radio Access Network (RAN) and Core Network (CN). Currently multiple proposals, such as extended access barring (EAB) and access class barring (ACB), have been broadly discussed in Third Generation Partnership Project (3GPP) to combat the problem of Random Access Channel (RACH) congestion. However, less effort has been put on the efficiency issue of uplink transmission for mMTC traffics featured with small data packets and infrequent transmissions. To address this problem, we present an enahnced grant-free access scheme for mMTC uplink transmissions based on the probability concept, where a type of specific resource called Probability-Based Access (PBA) channel is allocated with congestion probability indicated. Thus the mMTC device can initiate the uplink transmission based on the probability, i.e. data can be transferred directly on the PBA channel, or fall back to the legacy procedure by using contention-based random access scheme. The performance of the proposed scheme is evaluated by numerical simulations and its effectiveness and advantages are validated.

ACS Style

Dongyao Wang; Sung Hwan Kim; Xiaoqiang Zhu. Enhanced Probability-Based Grant-Free Uplink Data Transmission in 5G mMTC. Communications in Computer and Information Science 2019, 227 -239.

AMA Style

Dongyao Wang, Sung Hwan Kim, Xiaoqiang Zhu. Enhanced Probability-Based Grant-Free Uplink Data Transmission in 5G mMTC. Communications in Computer and Information Science. 2019; ():227-239.

Chicago/Turabian Style

Dongyao Wang; Sung Hwan Kim; Xiaoqiang Zhu. 2019. "Enhanced Probability-Based Grant-Free Uplink Data Transmission in 5G mMTC." Communications in Computer and Information Science , no. : 227-239.