• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2015년도 제51차 동계학술대회논문집 23권1호
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• pp.51-54
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• 2015

추천 시스템의 대표적인 연구 중 하나인 콘텐츠 기반 추천 시스템 연구는 TV 프로그램이나 영화의 줄거리, 장르, 리뷰 등의 콘텐츠의 메타데이터를 이용한다. 그러나 이러한 연구들은 콘텐츠 관련 정보에만 의존할 뿐, 시청자의 프로파일과 콘텐츠의 정보를 함께 고려하지 않는다. 본 논문에서는 시청자의 프로파일 중 연령과 콘텐츠의 정보인 프로그램의 줄거리를 활용한 TV 프로그램 추천 시스템을 제안한다. 본 추천 시스템은 시청자를 연령에 따라 분류한 후, LDA 알고리즘을 이용하여 시청자의 시청 TV 프로그램의 줄거리를 분류된 나이에 따라 각각의 줄거리 토픽 모델로 생성한다. 이를 기준으로 시청자가 원하는 시간대에 방송되는 프로그램들의 줄거리 토픽벡터와 시청자의 선호도 토픽벡터의 유사도를 비교해 가장 유사도가 높은 TV 프로그램을 시청자에게 추천하는 방식이다. 본 논문에서는 연구의 효용성을 검증하기 위해 줄거리만을 사용한 경우와 줄거리와 연령을 동시에 활용한 경우를 비교 실험하였다. 실험을 통해 프로그램의 줄거리만을 사용한 경우보다 연령을 동시에 활용한 경우의 추천 시스템 성능이 개선된 것을 확인할 수 있었다.

• Journal of KIISE:Software and Applications
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• 제31권2호
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• pp.153-163
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• 2004

Many recommender systems are based on Content-based Filtering and Social Filtering Both methods have their own advantages and disadvantages, and they complement each other rather than compete. So incorporating of both methods can make the better system and combination technique controls the quality of the entire recommender system. In this paper, we presented each user has his own tendency to decide which is the better recommendation for himself among the various recommendation results, and suggested the Personalized combination technique. To represent user tendency, we defined and used loyalty, diversity and pioneerity and showed by experiments that our combination technique is useful. This combination technique improved the average coverage 23% and for the ceiling 40%.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2000년도 추계학술발표논문집 (상)
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• pp.289-292
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• 2000

인터넷이 생활의 일부분이 되어감에 따라 인터넷상에서 이루어지는 전자상거래는 빠르게 발전하고 있다. 지금까지의 전자상거래는 고객이 요구하는 제품을 판매하는 단순한 형태였다. 하지만 앞으로의 전자상거래에서는 고객이 선호할 만한 제품을 예상하여 고객에게 해당 제품을 추천해 줌으로서 양질의 서비스를 제공하고 더 많은 이익을 창출 할 수 있는 전자상거래 시스템이 요구되고 있다. 본 논문에서는 전자상거래시스템에서 이용될 수 있는 추천시스템을 개발하기 위하여 추천시스템의 핵심이 되는 사용자간 유사도에 기초한 GroupLens의 협동적 필터링 알고리즘을 실제 Data Set을 통해서 실험하였다. 또한 Data Set을 분석하여 아이템을 대표할 수 있는 장르를 결정하여 전체 학습데이터로부터 대표장르에 속하는 데이터들만을 분리하여 학습데이터로 사용하는 추천시스템을 제안하였고, 실험을 통하여 제안한 추천시스템의 타당성을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
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• pp.11-12
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• 2023

본 논문에서는 상점 내 캡티브 포털을 활용하여 수집된 주문 정보 데이터를 바탕으로 사용자가 선호하는 메뉴를 추천하는 시스템을 제안한다. 이 시스템은 식품 관련 공공 데이터셋으로 학습된 단어 임베딩 모델(Word Embedding Model)로 메뉴명을 벡터화하여 그와 유사한 벡터를 가지는 메뉴를 추천한다. 이 기법은 캡티브 포털에서 수집되는 데이터 특성상 사용자의 개인정보가 비식별화 되고 선택 항목에 대한 정보도 제한되므로 기존의 단어 임베딩 모델을 추천 시스템에 적용하는 경우에 비해 유리하다. 본 논문에서는 실제 동일한 시스템을 사용하는 상점들의 구매 기록 데이터를 활용한 검증 데이터를 확보하여 제안된 추천 시스템이 Precision@k(k=3) 구매 예측에 유의미함을 보인다.

• Journal of Intelligence and Information Systems
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• 제18권4호
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Journal of Digital Contents Society
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• 제18권4호
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• Journal of the Korean Institute of Intelligent Systems
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• 제16권2호
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• pp.156-163
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• 2006

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.

• The Journal of the Korea Contents Association
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• 제11권12호
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• pp.40-51
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• 2011

The key issues of recommendation systems provide the contents satisfying the interests of users for the huge amounts of contents over internet. The existing recommendation system use the algorithms considering the users' profiles and context information to enhance the exactness of a recommendation. However, the existing recommendation system can't satisfy the requirements of service providers because the business models of service providers is not considered. In this paper, we propose the mobile recommendation system using the composite contexts and the recommendation weights applying the business model of service providers. The proposed system retrieves the contents of the contents providers using composite context information and apply the recommendation weights to recommend the suitable contents for the business models of service providers. Therefore, we provide the contents satisfying the consumption value of users and the business models of service providers to mobile users.

• The Journal of Society for e-Business Studies
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• 제18권2호
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• pp.279-299
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• 2013

The problem of choosing the right product that will best fit a consumer's taste and preferences extends to the field of electronic commerce. However, e-commerce has been able to create a technological proxy for the social filtering process, known as online recommender systems (RSs). RSs aid users in filtering products and decisions on matters relating to personal taste. RSs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RSs has focused on the accuracy of the algorithms, with little emphasis on user interface and perspectives. This study identified transparency and feedback as possible ways to effectively evaluate RSs from the user's perspective. Thus, this research focused on examining and identifying the roles of transparency and feedback in recommender systems and how they affect users' attitudes toward the system. Results of the study showed that both transparency and feedback positively and significantly affected perceived trust, perceived value of the process, and perceived enjoyment. Furthermore, we found that perceived trust, perceived value of the process, and perceived enjoyment positively and directly affected users' intentions to use/reuse a recommender system.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.982-983
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• 2020

추천 시스템은 사용자들의 과거 구매 이력 등을 학습해서 사용자들이 미래에 구매할 것 같은 상품을 추천한다. 대부분의 추천 시스템 관련 연구들은 사용자들과의 상호작용을 고려하지 않은 채 한 번의 모델 학습과 한 번의 추천만 수행하며, 사용자로부터 추천 결과에 대한 피드백을 받아서 더 나은 추천을 수행하려는 시도는 거의 이루어지지 않았다. 본 논문에서는 기존의 추천 모델들이 사용자와의 상호작용을 추가적으로 고려했을 때 어느 정도의 정확도 향상을 이룰 수 있는지에 대해서 분석한다. 특히 사용자와의 상호작용을 통해 사용자 취향의 다양성을 파악하고 이를 반영하여 더 나은 추천을 제공하는 방법에 대해서 논의한다.