• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.956-959
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• 2014

웹 2.0 의 폭발적인 성장과 스마트기기의 대중화 및 모바일 서비스의 활성화로 인하여 다양하고 방대한 양의 멀티미디어 콘텐츠가 보편화되었다. 따라서, 최근에 이를 효과적으로 활용하기 위한 다양한 연구가 수행되고 있다. 그러나, 사용자들은 아직도 수많은 멀티미디어 콘텐츠들 중에서 자신들이 원하는 콘텐츠를 찾는데 많은 어려움을 겪고 있다. 이에 따라, 사용자들의 올바른 의사결정을 도와주는 추천시스템에 대한 중요도가 나날이 급증하고 있다. 본 논문에서는 영화에 대해 사용자들이 남긴 리뷰로부터 감정 분석을 하고 분석된 각 사용자들의 감정 수치를 기반으로 영화추천 방법을 제안한다. 제안한 방법은 사용자들의 리뷰를 수집하고 각 사용자들의 감정 단어를 추출한다. 추출한 감정 단어들은 센티워드넷을 이용하여 사용자의 감정이 나타내는 정도를 분석한다. 분석된 사용자들의 감정 정보들을 바탕으로 사용자들에게 적절한 영화를 추천한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1293-1304
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• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.801-804
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• 2004

2001년 하반기 이후 디지털 TV 시대가 열리면서 채널의 수와 그에 따른 프로그램의 수가 폭발적으로 증가했다. 그리하여 기존의 방법으로는 시청자가 원하는 프로그램을 선택하는 것이 어려운 일이 되었다. 이 문제를 해결하는 방안으로서 pEPG(personalized Electronic Program Guide)가 많이 연구되어 왔으며 본 논문에서는 pEPG를 위한 추천 방법에 대해 연구하고자 한다. 기존의 추천 방법은 내용기반추천과 협업추천이 대표적인데, 이들은 어느 한족이 우월하다기 보다 각각의 단점을 상호보완해주는 관계에 있다. 각 추천 방법이 TV환경의 pEPG에 적용될 때는 어떤 장단점이 있는지 살펴보고, 이에 인구통계학적추천을 혼합한 기법을 제안한다.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.119-135
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• 2012

Among various recommendation techniques, neighborhood-based Collaborative Filtering (CF) techniques have been one of the most widely used and best performing techniques in literature and industry. This paper proposes new approaches that can enhance the neighborhood-based CF techniques by identifying a few best neighbors (the most similar users to a target user) more accurately with more information about neighbors. The proposed approaches put more weights to the users who have more items co-rated by the target user in similarity computation, which can help to better understand the preferences of neighbors and eventually improve the recommendation quality. Experiments using movie rating data empirically demonstrate simultaneous improvements in both recommendation accuracy and diversity. In addition to the typical single rating setting, the proposed approaches can be applied to the multi-criteria rating setting where users can provide more information about their preferences, resulting in further improvements in recommendation quality. We finally introduce a single metric that measures the balance between accuracy and diversity and discuss potential avenues for future work.

• Knowledge Management Research
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• v.21 no.3
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• pp.27-44
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• 2020

In order to solve the user's information overload problem, recommender systems infer users' preferences and suggest items that match them. The collaborative filtering (CF), the most successful recommendation algorithm, has been improving performance until recently and applied to various business domains. Visual information, such as book covers, could influence consumers' purchase decision making. However, CF-based recommender systems have rarely considered for visual information. In this study, we propose VizNCS, a CF-based deep learning model that uses visual information as additional information. VizNCS consists of two phases. In the first phase, we build convolutional neural networks (CNN) to extract visual features from image data. In the second phase, we supply the visual features to the NCF model that is known to easy to extend to other information among the deep learning-based recommendation systems. As the results of the performance comparison experiments, VizNCS showed higher performance than the vanilla NCF. We also conducted an additional experiment to see if the visual information affects differently depending on the product category. The result enables us to identify which categories were affected and which were not. We expect VizNCS to improve the recommender system performance and expand the recommender system's data source to visual information.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.459-460
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• 2023

기존의 추천시스템은 상품간 혹은 사용자 간의 유사도를 기반으로 작동한다. 하지만 이는 사용자가 유사한 상품 추천 속에 갇히게 되는 필터 버블의 문제와 추천시스템의 고질적인 문제인 데이터 희소성 문제를 피할 수 없게 된다. 따라서 본 연구에서는 사용자의 취향과 체형 정보를 반영하여 사용자의 평점을 예측하는 협업 필터링 기반 딥러닝 추천과 상품간 비유사성을 고려하여 사용자의 평점을 예측하는 내용 기반 추천을 혼합한 하이브리드 추천 모델을 구축하여 기존 추천시스템의 문제점을 해결하였다. 모델의 성능평가를 위해 인터넷 의류 쇼핑몰을 대상으로 유사한 이미지를 활용한 하이브리드 추천 모델과 NDCG 값을 비교하였고 유사도가 낮은 이미지를 활용한 모델이 더 우수한 성능을 보였다. 이는 다른 제품과는 달리 소비자가 의류를 구매할 경우 이미 구매한 상품과 유사한 상품보다는 유사하지 않은 상품을 구매할 가능성이 크다는 것을 보여준다.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.7-11
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• 2013

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.10-19
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• 2010

The progress of computers and Web has given rise to a rapid increase of the quantity of the useful information, which is making the demand of recommender systems widely expanding. Like in other domains, a recommender system in a digital library is important, but there are only a few studies about the recommender system of research papers, Moreover none is there in korea to our knowledge. In the paper, we seek for a way to develop the NDSL recommender system of research papers based on the survey of related studies. We conclude that NDSL needs to modify the way to collect user's interests from explicit to implicit method, and to use user-based and memory-based collaborative filtering mixed with contents-based filtering(CF). We also suggest the method to mix two filterings and the use of personal ontology to improve user satisfaction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.57-58
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• 2010

UCC 제작자에겐 기존의 추천 서비스와는 차별화된 저작권과 저작 목적 등을 고려한 별도의 추천서비스가 필요하다. 본 논문에선 UCC를 제작하는데 있어 발생하는 저작권문제를 효과적으로 해결하기 위해 UCC 제작 시 참조된 UCC들의 정보를 메타데이터의 reference 요소로 기재할 수 있도록 하였으며, UCC 제작 사용자에게 특화된 추천서비스를 제공하기 위해 제작된 UCC의 참조 데이터를 이용한 협업 필터링 기반의 추천 시스템을 구성하고 있다. 추천시스템은 메타데이터의 tag, reference 요소를 이용해 참조된 UCC 그룹군에서 제작자가 참조한 UCC와 유사한 참조 UCC를 추천 리스트로 만들어서 제공한다. 향후 본 시스템의 효율성 검증을 통해 UCC 제작에 있어 보다 효율적이고 제작자 편이성이 높은 제작자 맞춤형 UCC 추천 서비스가 IPTV, SmartTV등의 융합형 방송서비스 통해 제공될 수 있을 것 이다.