1990년대 중반에 협업 필터링의 출현으로 인하여 추천시스템에 관련된 연구가 늘어나게 되었다. 협업 필터링의 출현 이후 내용 기반 필터링, 협업 필터링과 내용 기반 필터링이 혼합된 하이브리드 필터링 등 새로운 기법들이 출현함으로써 2000년대에는 추천시스템의 연구가 눈에 띄게 증가하였다. 하지만 현재까지 추천시스템에 관련된 문헌들에 대한 리뷰와 분류가 체계적으로 되어있지 않다. 이와 같은 문제에 대한 해결방안으로써, 본 연구에서는 2001년부터 2010년도까지의 추천시스템에 관련된 문헌들 중 MIS Journal Ranking의 125개의 저널에서 추천시스템(Recommender system, Recommendation system), 협업 필터링(Collaborative Filtering), 내용 기반 필터링(Content based Filtering), 개인화 시스템(Personalized system) 등의 5가지 키워드로 제한하여 조사하였다. 총 37개의 저널에서 논문을 검색하였으며, 검색되어진 논문을 분석한 결과 추천시스템과 관련이 없는 논문을 제외한 총 187개의 논문을 선정하여 분석하였다. 이 연구에서는 그러나 컨퍼런스 논문, 석사, 박사학위 논문, 영어로 작성되지 않은 논문, 완성되지 않은 논문 등은 제외하였다. 본 연구에서는 187개의 논문을 분석하여 2001년부터 2010년까지의 각각의 년도 별 추천시스템의 연구에 대한 동향 분석, Journal별 추천시스템의 게재 분류, 추천시스템 어플리케이션의 사용 분야(책, 문서, 이미지, 영화, 음악, 쇼핑, TV 프로그램, 기타)별 분류 및 분석, 추천시스템에 사용된 데이터마이닝 기술(연관 규칙, 군집화, 의사 결정나무, 최근접 이웃 기법, 링크 분석 기법, 신경망, 회귀분석, 휴리스틱 기법)별 분류 및 분석을 수행하였다. 따라서 본 연구에서 제안한 각각의 분류 및 분석 결과들을 통하여 현재까지 추천시스템의 연구에 대한 연구 동향을 파악 할 수 있었으며, 분석결과를 통해 추천시스템에 관심이 있는 연구자와 전문가에게 미래의 추천시스템의 연구에 대한 가이드라인을 제시 할 수 있을 것이라고 기대한다.
1990년대 중반에 협업 필터링의 출현으로 인하여 추천시스템에 관련된 연구가 늘어나게 되었다. 협업 필터링의 출현 이후 내용 기반 필터링, 협업 필터링과 내용 기반 필터링이 혼합된 하이브리드 필터링 등 새로운 기법들이 출현함으로써 2000년대에는 추천시스템의 연구가 눈에 띄게 증가하였다. 하지만 현재까지 추천시스템에 관련된 문헌들에 대한 리뷰와 분류가 체계적으로 되어있지 않다. 이와 같은 문제에 대한 해결방안으로써, 본 연구에서는 2001년부터 2010년도까지의 추천시스템에 관련된 문헌들 중 MIS Journal Ranking의 125개의 저널에서 추천시스템(Recommender system, Recommendation system), 협업 필터링(Collaborative Filtering), 내용 기반 필터링(Content based Filtering), 개인화 시스템(Personalized system) 등의 5가지 키워드로 제한하여 조사하였다. 총 37개의 저널에서 논문을 검색하였으며, 검색되어진 논문을 분석한 결과 추천시스템과 관련이 없는 논문을 제외한 총 187개의 논문을 선정하여 분석하였다. 이 연구에서는 그러나 컨퍼런스 논문, 석사, 박사학위 논문, 영어로 작성되지 않은 논문, 완성되지 않은 논문 등은 제외하였다. 본 연구에서는 187개의 논문을 분석하여 2001년부터 2010년까지의 각각의 년도 별 추천시스템의 연구에 대한 동향 분석, Journal별 추천시스템의 게재 분류, 추천시스템 어플리케이션의 사용 분야(책, 문서, 이미지, 영화, 음악, 쇼핑, TV 프로그램, 기타)별 분류 및 분석, 추천시스템에 사용된 데이터마이닝 기술(연관 규칙, 군집화, 의사 결정나무, 최근접 이웃 기법, 링크 분석 기법, 신경망, 회귀분석, 휴리스틱 기법)별 분류 및 분석을 수행하였다. 따라서 본 연구에서 제안한 각각의 분류 및 분석 결과들을 통하여 현재까지 추천시스템의 연구에 대한 연구 동향을 파악 할 수 있었으며, 분석결과를 통해 추천시스템에 관심이 있는 연구자와 전문가에게 미래의 추천시스템의 연구에 대한 가이드라인을 제시 할 수 있을 것이라고 기대한다.
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, mu...
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.
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.
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문제 정의
In this paper, we classify comprehensive review of literatures on recommender systems that were published in academic journals from 2001 to 2010, to obtain insight on recommender systems. This paper is organized as follows:
Recommender systems have attracted the attention of academics and practitioners. In this paper, we have identified 187 articles on recommender systems, which are published from 2001 to 2010 to understand the trend of recommender systems-related research and to provide practitioners and researchers with insight and future direction on recommender systems. The results represented in this paper have several significant implications:
The aim of study is to understand the trend of recommender system researches by examining the published literatures, and to provide practitioners and researchers with insight and future direction on recommender systems.
제안 방법
The framework includes recommendation fields and data mining techniques. In this paper, we classify the reviewed articles into eight categories of recommendation fields and eight categories of data mining techniques. Graphical classification framework for recommender systems articles are shown in [Figure 1].
The framework includes recommendation fields and data mining techniques. In this paper, we classify the reviewed articles into eight categories of recommendation fields and eight categories of data mining techniques.
The selected criteria and evaluation framework is shown in [Figure 2]. The set of articles was analyzed by year of publication, by journals in which the articles were published, and by recommendation fields and data mining techniques.
대상 데이터
We selected a total of 187 articles from 37 journals. According to classification framework, the selected articles were classified.
후속연구
We hope that result of this study will emphasize the importance of recommender systems and provide both academics and practitioners with insight on recommender system research.
참고문헌 (20)
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