• Smart Media Journal
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• v.9 no.1
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• pp.45-50
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• 2020

With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.323-338
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• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.657-659
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• 2023

최근 스마트폰과 같은 디지털 기기의 보급과 함께 개인화, 맞춤형 서비스의 수요가 늘어나면서 추천 서비스가 주목을 받고 있다. 세션 기반(Session based) 추천 시스템은 사용자의 아이템 선호에 따른 순서 정보를 고려한 학습 추천 모델로, 다양한 산업 분야에서 사용되고 있다. 세션 기반 추천 시스템 중 SASRec(Self-Attentive Sequential Recommendation) 모델은 MC/CNN/RNN 기반의 기존 여러 순차 모델들에 비하여 효율적인 성능을 보인다. 본 연구에서는 SASRec 모델의 하이퍼파라미터 중 배치 사이즈(Batch Size), 학습률 (Learning Rate), 히든 유닛(Hidden Unit)을 조정하여 실험함으로써 하이퍼파라미터에 의한 성능 변화를 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.5-6
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• 2023

본 연구에서는 언텍트 소비가 일반화됨에 따라 소상공인들을 지원하기 위해 캡티브-포털을 활용하여 주문하는 등의 시스템을 구축하고 있으며, 이에 상권 내 방문자들의 주문 정보를 기반으로 개인의 선호나 취향을 고려하고 기존 방문 순서를 고려하여 다음 방문지를 추천할 수 있는 모델을 개발하고자 한다. 모델 개발을 위한 데이터셋으로는 캡티브-포털을 통해 수집되는 변수 항목과 유사한 위치기반 SNS 데이터인 Foursquare 데이터를 활용했다. 본 논문에서는 데이터셋의 변수 중 상호명을 기반으로 22개의 행동 유형 카테고리로 묶어 현재 행동 유형 이후에 다음에 이어질 행동 유형을 예측하는 것을 제안한다. 개인 별 세션 기반의 데이터셋을 LightMove 알고리즘을 활용하여 행동유형 예측을 임베딩 차원의 변경하여 실험한 결과 500차원에서 Top-5가 82.72의 성능을 보임을 확인했다. 향후 국내 상권에 맞는 방문지 추천 시스템이 개발된다면 방문지 추천을 활용하여 다양한 마케팅 전략을 수립이 가능해질 수 있고, 이를 통해 지역 상권이 활성화될 것으로 기대된다.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.586-594
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• 2004

Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.

• Education of Primary School Mathematics
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• v.16 no.3
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• pp.229-250
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• 2013

The purpose of this study is analyzing 'observation and recommendation letter by teacher', which is being submitted to screen and enhance the utilization of gifted students in accordance with recently introduced gifted students observation, recommendation and screening system. For the purpose, this study will provide with objective securing plan of 'observation and recommendation letter by teacher' by developing an optimum evaluation model. The research findings were as follows: First, the result of analysis on the mathematically gifted students behavior characteristic as appeared in 'observation and recommendation letter by teacher' suggested that the recommending teachers have the tendency of giving superficial statement instead of giving concrete case description. When it was analyzed for frequency by the 'observation and recommendation letter by teacher' analysis framework devised by the author, the teachers showed the tendency of concentrating on specific questions. Meanwhile, there was a tendency that teachers concentrate on specific gifted behavior characteristic or area for which concrete case had been suggested. The reason is believed that such part is easy to observe and state while others are not, or, teachers did not judge the other part as the characteristic of gifted students. Second, the gifted students behavior characteristics as appeared in 'observation and recommendation letter by teacher' were made into scores by Rubric model. When the interrater reliability was analyzed based on these scores, the correlation coefficient of 1st scoring was .641. After a discussion session was taken and 2nd scoring was done 3 weeks later, the correlation coefficient of 2nd scoring increased to .732. The reason is believed that; i) the severity among scorers was adjusted by the discussion session after the 1st scoring, ii) the scorers established detail judgment standard on various situations which can appear because of the descriptive nature, and, (iii) they found a consensus on scoring for a new situation appeared. It implies that thorough understanding and application of scorers on evaluation model is as important as the development of optimum model for the differentiation of mathematically gifted elementary students.

• The Journal of Korean Academic Society of Nursing Education
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• v.6 no.2
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• pp.327-344
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• 2000

The purpose of this survey study is to guide the direction of the RN-BSN program in Korea by analyzing (1) the philosophy and objectives (2) curriculum (3) and appraisal method, achievement test and self-directed learning. In this study, subjects consisted of 20 department of nursing in University and 20 RN-BSN programs in Korea. The Survey was conducted from September 1999 through May 2000 by mail and FAX. 1) Educational philosophy and objectives of 5 RN-BSN programs have curricular based philosophy. Most popular philosophies were revealed that nursing is oriented role function, human relation, and integrated application. 9 RN-BSN programs have curricular based objectives. There are including of knowledge, applying the new technology, under-standing of human being family community, application of nursing process, leadership, nursing ethics, and participation in research. 2) In RN-BSN programs, total mean credits through general college courses for earning the degree of BSN is 21.5 and total mean credits through the nursing area for earning the degree of BSN is 71.4. In RN-BSN programs, total mean credits through clinical practice for earning the degree of BSN is 5.94. 25.00 of mean credits was earned through achievement test(6.00~37.00). Therefore, this research suggests some recommendation for the development of curriculum of RN-BSN program that was required to do some alterations. And the various and other methods of earning credit should be developed. That is, the students will earn credits, accredited examination of University, advanced placement examination, case study, self-report, self-directed learning and achievement tests, portfolio review session and so on. And the RN-BSN courses are delivered to many areas by teleconferencing system, computer network(EdNet or Internet etc), CD-ROM Title, VOD (video on demand) and other methods.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.