• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.644-645
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• 2024

COVID-19 유행 이후 배달 음식의 수요가 증가했다. 이러한 배달 음식은 재료나 영양성분 파악이 어려운 경우가 많아 원하는 음식 조건이 있거나 영양 균형을 필요로 하는 개인이 곤란을 겪는다는 문제점이 있다. 따라서 맞춤형 음식을 원하는 개인이 손쉽게 요리를 할 수 있는 방안을 마련하고자, 여러 머신러닝 알고리즘을 결합한 하이브리드 모델을 이용한 레시피 추천시스템을 구현했다. 구현 후에는 웹사이트를 제작하여 직접 적용해봄으로써 그 활용성을 확인했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1270-1271
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• 2011

소셜 네트워크 서비스는 가트너에서 2011년에 이어 2012년에도 각광받을 기술의 하나로 선정된 만큼 미래 인터넷의 핵심 키워드 중 하나로도 뽑히며, 엔터테인먼트, 검색, 방송, 커머스 등의 여러 가지 서비스와 직접 연결된다. 이러한 소셜 네트워크 서비스 가운데 하이브리드형 서비스는 사용자의 정보를 관리 및 파악하여 사용자가 원하는 제품을 예측하고 추천해주고 있으며, 이를 위해 그래프 마이닝 기술을 적용하고 있다. 하지만 그래프 마이닝 기술은 아직 복잡한 그래프 구조의 데이터에서 정보를 추출하기에 제약사항들이 발생하므로 이에 대하여 많은 연구가 활발히 이루어지고 있다. 이러한 그래프 마이닝 기술을 나아가 더 발전시켜 활용하면 기존의 하이브리드형 서비스에서 사용자의 정보를 파악하여 충성도를 높여줄 뿐 아니라 기업에서의 타켓 마케팅과 원투원 마케팅을 가능하게 해주고 기존 사용자에 대한 교차 판매와 격상판매의 전략들을 도출할 수 있을 것이다.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.163-171
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• 2020

This study analyzed the contents and changes by year of metadata research that appeared in the study of domestic movie curation from the viewpoint of art management. The research method used thesis search site to search 'movie' and 'metadata' as keywords, and analyzed them in 4 stages of change according to the research trend by year, purpose of research content, analysis by use, and type of recommendation method. As for research results, movie metadata research is highly interested in user-side research, and is developing from an introduction stage to an evolutionary stage of recommendation to a sharing and participation stage. It was concluded that movie curation evolved into 6 stages: search support, content-based, collaborative filtering, hybrid, artificial intelligence, and curation.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.615-620
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• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.646-653
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• 2016

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.777-785
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• 2009

Collaborative filtering is a technique used to predict whether a particular user will like a particular item. User-based or item-based collaborative techniques have been used extensively in many commercial recommender systems. In this paper, a hybrid collaborative filtering method that combines user-based and item-based methods using a low-dimensional linear model is proposed. The proposed method solves the problems of sparsity and a large database by using NMF among the low-dimensional linear models. In collaborative filtering systems the methods using the NMF are useful in expressing users as semantic relations. However, they are model-based methods and the process of computation is complex, so they can not recommend items dynamically. In order to complement the shortcomings, the proposed method clusters users into groups by using NMF and selects features of groups by using TF-IDF. Mutual information is then used to compute similarities between items. The proposed method clusters users into groups and extracts features of groups on offline and determines the most suitable group for an active user using the features of groups on online. Finally, the proposed method reduces the time required to classify an active user into a group and outperforms previous methods by combining user-based and item-based collaborative filtering methods.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.515-526
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• 2019

In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.1
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• pp.127-135
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• 2005

Recently, as the concrete structures are becoming bigger, higher, longer and more special, high strength concrete is demanded. But the fracture behavior of high strength concrete is shown more brittle than that of the normal strength concrete. Therefore, in order to improve the brittle fracture behavior and crack propagation resistance, ACI Committee363 has been recommend the use of fiber reinforced concrete which showed superior property against the crack propagation resistance. On the other hand, bridges, concrete pavements and railroads etc. have been exposed to the repetition loading at least several million times during the service life. Therefore, fatigue load is dominantly most of all, but it is very difficult to estimate the suitable fatigue strength calculated by fatigue load. In this research, in order to examine the fatigue behavior of hybrid fiber reinforced high strength concrete, the static and fatigue tests were carried out. And from these results, it was estimated the fatigue strength of hybrid fiber reinforced high strength concrete.