• Journal of Gifted/Talented Education
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• v.25 no.6
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• pp.857-879
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• 2015

This study investigated the relationships among middle school students' perceptions on the roles of online tutor, their deep learning, achievement, and overall evaluation of learning experiences in the context of inquiry based online gifted mathematics and science learning. For this purpose, 249 middle school students who took online course were surveyed about their perceptions on the degree to which their tutor performed the roles as an online tutor. The students were also asked about the activities which indicate deep learning approaches and overall course experiences such as the level of satisfaction, understanding and engagement in the course. The regression analyses were conducted to examine the relationships of students' perceptions on the roles of online tutor, deep learning, achievement, and overall course experiences. The results first showed that the roles of online tutor which affects students' deep learning approach such as high-order learning, integrative learning, reflective learning were the role as a subject matter and evaluation expert. Among the sub variables of deep learning approach the variable that was related to students' overall achievement was the use of high-order learning strategy. Second, the achievement in inquiry task was related to the role of tutor as a guide of learning process and method. Third, students' overall course evaluations such as the level of satisfaction, understanding and engagement were not related to any role of tutor.

• Korean Journal of Human Ecology
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• v.18 no.5
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• pp.1063-1075
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• 2009

This study intends to provide foundational data for enhancing the welfare of customers purchasing digital products through analyzing the notes from written on-line reviews. The data used for the analysis are 6,342 on-line reviews for cell phones and digital cameras released from November, 2007 until April, 2008, which was posted on Naver Knowledge Shopping from November, 2007 until June, 2008. Through the on-line reviews, this article analyzed the evaluations on the digital products' hardware, software, design, service, price, and other criteria and the customers' emotional experience in the process of purchase, use, and possession. According to the results of the analysis, negative evaluation and emotional experience were originated from the company's information provision methods and purchase process. In addition, insufficient information searches in the process of online purchases, consumers' low right consciousness, and impolite on-line reviews were also problematic. Customers' evaluations and emotional experiences on digital products were conducted in a complex way. Based on that, this research makes suggestions in the company's marketing, customer education, and theoretical aspect.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.148-158
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• 2020

With the limited product information available in the m-shopping context, product-naming strategies affect consumer choices by expressing the key product features or the brand's images. Given the increasing dominance of the mobile commerce in consumption across various product categories, few studies have examined the role of product naming in consumer choices in the m-shopping. In filling the research gap, this study empirically analyzes the influence of the perceived diagnosticity of product names in m-shopping on consumer attitude towards the product. Moreover, the study tests the moderating influences of the individual characteristics of consumers (i.e., age, gender, and m-shopping experience) in the dynamics of the perceived diagnosticity impacting the product evaluations. The results of the study using an online survey reveal that the perceived diagnosticity of the product names significantly increases consumer attitude towards the product. Additionally, the moderating effects of gender, age, and m-shopping experience are all found significant: (1) The positive influence of the perceived diagnosticity of the product names is greater for males than for females. (2) The older the respondent, the more statistically significant the positive influence on diagnosticity. (3) The more respondents having m-shopping experience, the more positive the impact of the diagnosticity. Implications and limitations of the study are discussed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.300-314
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• 2018

This study, expending the existing concept of hostile media perception to biased media perception, empirically examined the effects of TV audience's political disposition(liberal, neutral, conservative) and broadcasting company's political disposition perceived by the audience(liberal, neutral, conservative) on evaluations of the broadcasting channel(interestedness, diversity, creativity, reliability, usefulness, fairness, public interest). With the nationwide online survey data from 624 adult audiences, a broadcasting company evaluated as the most neutral in political disposition at the moments of data gathering was selected and respondents' evaluations of the broadcasting channel were analyzed in a two-way factorial multiple analysis of covariance(MANCOVA). The results showed that the perceived political disposition of the broadcasting company affects audiences' evaluations of the channel while audiences' their own political dispositions do not. In addition, due to the interaction effects between audience's and broadcasting company's political dispositions, congruency in political dispositions led to positive evaluations(congenial media perceptions) while incongruency led to negative ones(hostile media perceptions). Based on these results, theoretical and empirical implications were discussed in terms of the formation of attitudes toward TV broadcasting channels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3192-3212
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• 2016

With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2346-2362
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• 2016

As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.715-725
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• 2020

In this paper, we propose an optimization scheme for a k-Nearest Neighbor(k-NN) query, which finds k objects closest to the query in the high dimensional feature vectors. The k-NN query is converted and processed into a range query based on the range that is likely to contain k data. In this paper, we propose an optimization scheme using DNN model to derive an optimal range that can reduce processing cost and accelerate search speed. The entire system of the proposed scheme is composed of online and offline modules. In the online module, a query is actually processed when it is issued from a client. In the offline module, an optimal range is derived for the query by using the DNN model and is delivered to the online module. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.115-120
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• 2017

Online reputation systems for social network services(SNS) aggregate users' feedback and estimate the reputation of contents or providers. The aim of this research is to enhance credibility of the online reputation system on the SNS based e-Commerce(we called it as social commerce). SNS users usually refer to evaluations from other users who bought the products before. Most social commerce sites provide reputation system to help their customer make a decision, but sometimes we can't believe the reputation because the reputation is too subjective and the seller can deceive the customer for sales promotion. Threrefore, we usually use just the average value to show the general customer's evaluation result. We applied collaborative filtering method to give more weighting to the users who have evaluated correctly in the past. As a result, we could get more accurate evaluation results by considering each customers' credibility value that was computed by collaborative filtering.