• Journal of Oriental Neuropsychiatry
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• v.27 no.4
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• pp.235-248
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• 2016

Objectives: The aim of this review was to investigate whether evidence of complementary and alternative medicine (CAM) was reflected in clinical practice guidelines (CPGs) for insomnia based on relevant clinical trials. Methods: We conducted a systematic search on domestic and international CPG databases and medical databases. In addition, we conducted manual searches of relevant articles. Three authors independently searched and selected relevant studies; any disagreement was resolved by discussion. We extracted and analyzed the following data: published language, country, development group, participants, interventions, presence or absence of recommendations for CAM, level of evidence, grade of recommendation for CAM, and methods of development. Results: We identified 8,241 records from domestic and international databases, and 22 CPGs were included. Eleven of the 22 CPGs mentioned CAM interventions including herbal medicine, relaxation, acupuncture moxibustion, Tai Chi, meditation, hypnosis, biofeedback, Tuina, and external herbal medicine. However, most of the CPGs indicated 'no recommendation' or 'weak recommendation' for CAM interventions. Only Valeriana dageletiana Nakai and relaxation were considered to have experimental evidence. Valeriana dageletiana Nakai was recommended for improvement of sleep latency, sleep maintenance, total sleeping time, and sleep cycle. Relaxation was recommended as effective intervention for relieving physical and psychological arousal. Conclusions: Despite systematic reviews and randomized controlled trials on CAM for insomnia, most of the CPGs for insomnia did not reflect the evidence obtained. Further CPGs for insomnia should be developed by considering the current advanced studies in the field of CAM.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.346-354
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• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.249-254
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• 2013

The number of channels are increased into several hundreds of channels when coming out the digital broadcasting era. In this environment, viewers searching for programs will be very difficult to do. In addition, recent popularization of smart devices are receiving the services that they previously had not been given to. A TV program recommended a system that has been studied as a way to solve these problems. However, most studies have been studied in most web-based research results when applied to broadcast TV for TV program recommendations. In particular, the combination of the current members who watch TV are not considered. In this paper, the environment and TV viewers are considering a combination of the members of the TV program's recommended system proposal. In order to make a group deal successful, we employ the face recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.161-175
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• 2024

In this paper, we examined the causes of privacy concern and related factors in personalized recommendation service of OTT. On the basis of the 'Big Five Personality model,' we established factors such as agreeableness, neuroticism, conscientiousness, extraversion, and openness to experience. Additionally, we established factors such as accuracy, diversity, and novelty of OTT recommendation's services, and perceived transparency. we analyzed the relationship between privacy concern, service benefit, and intention to give personal information. Finally, we analyzed the mediating effect of service benefits on the relationship between privacy concern and intention to give personal information. The results of this study showed that (1) neuroticism, extraversion and openness to experience had the significant effects on privacy concerns, (2) perceived transparency had the significant effects on privacy concern, 3) privacy concern and service benefit had the significant effect on intention to give personal information, and (4) as a result of multi-group analysis towards low and high groups to verify the moderating effect by service benefits, a significant difference was observed between privacy concern and intention to give personal information. The findings of the study are expected to help the OTT firms' understanding towards users' privacy protection behaviors.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of Gifted/Talented Education
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• v.21 no.2
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• pp.407-426
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• 2011

This study is to examine the perceptions and attitudes of parents about the teachers' recommendation system through their observations to identify the gifted. We conducted surveys for parents who had elementary school children in Busan metropolitan city and analysed the results to find out about the differences among parents groups in terms of their monthly incomes and opinions on teachers' observation and nomination for the gifted. The results are following; First, approx. 80% of the respondents recognized the changes in the giftedness identification system. Also, 40.5% of the parents positively thought about the teachers' observation and nomination system (positive group), whereas 32.3% of the parents showed their negative opinions about the system (negative group). There were also 27.2% of the parents who did not make their decisions (no-decision-making group). Second, most of the parents accepted that the teachers who were professionally trained in gifted education should be the persons for observing and nominating gifted students. However, significant differences were shown among the different monthly income groups of the parents in regards to who should be the person taking charge of recommending the gifted and how trustworthy this person could be. Third, the positive parents' group mostly expected that as the teachers' observation and nomination system is adopted, expenses for private education would decrease, whereas the negative group and no-decision-making group thought that the opposite result would occur. These results will be helpful for the successful adoption of teachers' observation and recommendation system to identify the gifted in the field of education.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.215-223
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• 2019

The study aims to investigate the recommendation and revisit intentions of foreign tourists in Ho Chi Minh city, Vietnam through their satisfaction with local foods. The study proposed the group of five attributes for food image: taste, health concern, price, serving style, vendor/ restaurant staffs. The relationship between these attributes of food image and food satisfaction, as well as the one between food satisfaction and behavioral intentions were investigated. To ensure a high ratio of answers, a face-to-face survey was conducted in famous places at Ho Chi Minh city. Data with 210 foreign tourists. The study uses the methods of descriptive statistics, EFA, Cronbach Alpha and regression. The results showed that Five attributes of food image were chosen for the research, being taste, health concern, price, serving style and vendors/ restaurant staffs. All of these attributes showed a positive relationship with satisfaction. Among five factors, taste had the most impact on food satisfaction. Through the analysis of several attributes of food images, this study provides managerial implications for tourism marketers in researching the positive influence of food image on tourists' satisfaction which leads to their positive word-of-mouth and return to the tourism place.