• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.465-470
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• 2004

Web Information Predictive Systems have the restriction such as they need users profiles and visible feedback information for obtaining the necessary information. For overcoming this restrict, this study designed and implemented Dynamic Web Information Predictive System using Ensemble Support Vector Machine to be able to predict the web information and provide the relevant information every user needs most by click stream data and user feedback information, which have some clues based on the data. The result of performance test using Dynamic Web Information Predictive System using Ensemble Support Vector Machine against the existing Web Information Predictive System has preyed that this study s method is an excellence solution.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.117-120
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• 2020

본 논문은 인공지능 메이커 교육과 관련한 요소를 논문 네트워크 키워드 분석과 다양한 빅데이터를 종합하여 핵심용어를 선정 후 인공지능 메이커 교육을 시스템 다이내믹스의 Vensim프로그램으로 인과지도(Casual Loop Diagramming)를 구조분석(모델의 구조)하여 예측 결과를 토대로 향후 미래 상황 추출 및 정책 결정 연구에 영향을 기여한다. 연구 결과 인공지능 교육 정책은 추후 인공지능 교육과 메이커 교육을 융합한 교육 관련 산업이 증대할 것으로 예측되며 교육 경쟁력 향상과 창의적 인재 양성, OTT를 이용한 인공지능 교육 콘텐츠 향상으로 학습에 활용성이 증대하게 된다. 또한 인공지능 교육 정책은 프로그래밍 교육으로 연결되어 성장기 학습자들의 사고력과 정서 발달에 도움 되며 다양한 교재 및 기기 등장으로 인한 학습에 다양성 역시 증가할 것으로 예측된다. 학교 차원에서는 교수·연구 지원 활동이 증가하여 수업 전문성을 가진 교사가 늘어나 학교 교육의 질은 확대되고 학부모는 인공지능 교육 정책에 긍정적으로 된다. 시스템 다이내믹스는 구조가 형태를 결정짓는다는 세계관에 기초하여 피드백 루프와 동태적 형태 유형을 파악하며 다양한 가능성이 존재하게 된다. 이는 추후 다양한 연구를 통해 인공지능 교육 정책 인과지도의 확대로 연결될 수 있음을 암시하며 본 논문을 통해 인공지능 교육 연구 확산에 시발점이 되었으면 한다.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.145-147
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• 2004

본 논문에서는 무선인터넷 환경에 적합한 개인화된 상품추천에이전트를 제안한다. 기존에 유선인터넷상의 많은 개인화 추천시스템에서는 초기 사용자 모델링을 위해 사용자에게 수많은 질의를 하고 응답을 요구하였다. 그러나 이러한 방식은 무선인터넷 환경에서 정보 전송량에 따른 높은 사용요금을 고려할 때 적용하기 힘든 방식이다. 본 제안 시스템은 사용자의 Social data률 이용하여 사용자를 비슷한 연령과 성별 그룹으로 나누고, 해당 그룹에서 구매율이 높은 상품을 우선 제시한 후, 사용자 행동을 모니터링 하여 암시적(Implicit)피드백을 통해 프로파일을 생성함으로써, 번거로운 질의-응답 과정 없이도 초기 사용자 모델링을 수행할 수 있다. 프로파일 생성 이후에는 이를 기반으로 하여 사용자몰 유사한 취향을 가진 그룹으로 다시 군집화한 후 협력적 추천을 하게 되며, 프로파일에는 해당 상품의 최종 카테고리명과 키워드를 수집함으로써, 상품의 브랜드와 규격정보를 반영한 추천이 가능하다. 또한 추천 상품과 사용자의 구매데이터와의 비교를 수행하여 사용자가 해당상품을 구매하였을 경우, 상품에 대한 취향정보는 그대로 유지하고 관련 상품을 추천하되, 구매한 상품이 중복 추천되지 않도록 하였다. 시스템 평가를 위해 프로토타입을 구현하여, 다수의 사용자에게 시스템을 이용하며 관심품목을 체크하도록 하였고. 추천횟수가 반복되며 히트율이 증가하는 결과를 통해 시스템의 학습속도와 성능을 평가하였다. 그리고 쇼핌몰에서 구매경험이 있는 사용자의 기존 구매데이터와 Social data를 이용한 초기 제시상품을 역으로 비교하여 오랜 시간과 비용 발생 없이도 초기 프로파일 생성의 유효성을 증명하였다. 포함하는 XML 질의에 대해서도 웹에서 캐쉬를 이용한 처리가 효율적임을 확인하였다.키는데 목적이 있다.RED에 비해 향상된 성능을 보여주었다.웍스 네트워크상의 다양한 디바이스들간의 네트워크 다양화와 분산화 기능을 얻을 수 있었고, 기존의 고가의 해외 솔루션인 Echelon사의 LonMaker 소프트웨어를 사용하지 않고도 국내의 순수 솔루션인 리눅스 기반의 LonWare 3.0 다중 바인딩 기능을 통해 저 비용으로 홈 네트워크 구성 관리 서버 시스템 개발에 대한 비용을 줄일 수 있다. 기대된다.e 함량이 대체로 높게 나타났다. 점미가 수가용성분에서 goucose대비 용출함량이 고르게 나타나는 경향을 보였고 흑미는 알칼리가용분에서 glucose가 상당량(0.68%) 포함되고 있음을 보여주었고 arabinose(0.68%), xylose(0.05%)도 다른 종류에 비해서 다량 함유한 것으로 나타났다. 흑미는 총식이섬유 함량이 높고 pectic substances, hemicellulose, uronic acid 함량이 높아서 콜레스테롤 저하 등의 효과가 기대되며 고섬유식품으로서 조리 특성 연구가 필요한 것으로 사료된다.리하였다. 얻어진 소견(所見)은 다음과 같았다. 1. 모년령(母年齡), 임신회수(姙娠回數), 임신기간(姙娠其間), 출산시체중등(出産時體重等)의 제요인(諸要因)은 주산기사망(周産基死亡)에 대(對)하여 통계적(統計的)으로 유의(有意)한 영향을 미치고 있어 $25{\sim}29$세(歲)의 연령군에서, 2번째 임신과 2번째의 출산에서 그리고 만삭의 임신 기간에, 출산시체중(出産時體重) $3.50{\sim}3.99kg$사이의 아

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.