Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Literature Review and Classification of Recommender Systems on Academic Journals

추천시스템관련 학술논문 분석 및 분류

  • Park, Deuk-Hee (School of Management and Management Research Institute, Kyunghee University) ;
  • Kim, Hyea-Kyeong (School of Management and Management Research Institute, Kyunghee University) ;
  • Choi, Il-Young (School of Management and Management Research Institute, Kyunghee University) ;
  • Kim, Jae-Kyeong (School of Management and Management Research Institute, Kyunghee University)
  • 박득희 (경희대학교 경영대학 & 경영연구원) ;
  • 김혜경 (경희대학교 경영대학 & 경영연구원) ;
  • 최일영 (경희대학교 경영대학 & 경영연구원) ;
  • 김재경 (경희대학교 경영대학 & 경영연구원)
  • Received : 2010.12.26
  • Accepted : 2011.01.06
  • Published : 2011.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

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.

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.

Keywords

References

  1. Anders, U. and O. Korn, "Model Selection in Neural Networks", Neural Networks, Vol.12, No.2(1999), 309-323. https://doi.org/10.1016/S0893-6080(98)00117-8
  2. Basu, C., H. Hirsh and W. Cohen, "Recommendation as Classification : Using Social and Content-based Information in Recommendation", In Proceedings of the 15th National Conference on Artificial Intelligence, (1998), 714-720.
  3. Berry, M. J. A and G. S. Linoff, Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Second Edition, Wiley, 2004.
  4. Cai, D., X. He, J. R. Wen, and W. Y. Ma, "Block-level Link Analysis", Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, (2004), 440-447.
  5. Cho, Y. H., J. K. Kim and S. H. Kim, "A personalized Recommender System Based on Web Usage Mining and Decision Tree Induction", Expert System Applications, Vol.23, No.3(2002), 329-342. https://doi.org/10.1016/S0957-4174(02)00052-0
  6. Claypool, M., A. Gokhale, T. Miranda, P. Murnikov, D. Netes and M. Sartin, "Combining Content-based and Collaborative Filters in an Online Newspaper", Proceedings of the ACM SIGIR'99 Workshop on Recommender Systems, 1999.
  7. Frias-Martinez, E., G. Magoulas, S. Y. Chen and R. Macredie, "Automated User Modeling for Personalized Digital Libraries", International Journal of Information Management, Vol.26, No.3(2006), 234-248. https://doi.org/10.1016/j.ijinfomgt.2006.02.006
  8. Frias-Martinez, E., S. Y. Chen and X. Liu, "Evaluation of a Personalized Digital Library Based on Cognitive Styles : Adaptivity vs. Adaptability", International Journal of Information Management, Vol.29, No.1(2009), 48-56. https://doi.org/10.1016/j.ijinfomgt.2008.01.012
  9. Herlocker, J. L. and J. A. Konstan, "Content-independent Task-focused Recommendation", IEEE Internet Computing, Vol.5, No.6(2001), 40-47. https://doi.org/10.1109/4236.968830
  10. Kim H. K., J. K. Kim, and Y. U. Ryu, "Personalized Recommendation over a Customer Network for Ubiquitous Shopping", IEEE Transactions on Services Computing, Vol.2, No.2 (2009), 140-151. https://doi.org/10.1109/TSC.2009.7
  11. Kim, J. K., H. K. Kim, H. Y. Oh and Y. U. Ryu, "A group Recommendation System for Online Communities", International Journal of Information Management, Vol.30, No.3 (2010), 212-219. https://doi.org/10.1016/j.ijinfomgt.2009.09.006
  12. Kim, J. K., Y. H. Cho, W. J. Kim, J. R. Kim and J. H. Suh, "A Personalized Recommendation Procedure for Internet Shopping", Electronic Commerce Research and Applications, Vol.1, No.3-4(2002), 301-313. https://doi.org/10.1016/S1567-4223(02)00022-4
  13. Lihua, W., L. Lu, L. Jing, and L. Zongyong, "Modeling User Multiple Interests by an Improved GCS Approach", Expert Systems with Applications, Vol.29, No4.(2005), 757-767. https://doi.org/10.1016/j.eswa.2005.06.003
  14. Lopez-Nores, M., J. Garca-Duque, A. Fernandez- Vilas, R. P. Daz-Redondo, J. Bermejo-Mu noz, "A Flexible Semantic Inference Methodology to Reason about User Preferences in Knowledge-based Recommender Systems", Knowledge-Based Systems, Vol.21, No.4(2008), 305-320. https://doi.org/10.1016/j.knosys.2007.07.004
  15. Malhotra, N. K., Marketing Research : an Applied Orientation, Fifth Edition, Pearson Education Inc, 2007.
  16. Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom and J. Riedl, "GroupLens : an Open Architecture for Collaborative Filtering of Netnews", Computer Supported Cooperative Work Conf, 1994.
  17. Sarwar, B., G. Karypis, J. A. Konstan and J. Riedl, "Application of Dimensionality Reduction in Recommender System-a Case Study", Proceedings of the ACM WebKDD-2000 Workshop, 2000a.
  18. Sarwar, B., G. Karypis, J. A. Konstan, and J. Riedl, "Analysis of Recommendation Algorithms for E-Commerce", Proceedings of the ACM E-Commerce, (2000b), 158-167.
  19. Schafer, J. B., A. Joseph and R. John, "E-Commerce Recommendation Applications", Data Mining and Knoweldge Discovery, Vol.5, No.1-2(2001), 115-153. https://doi.org/10.1023/A:1009804230409
  20. Shardanand, U. and P. Maes, "Social Information Filtering : Algorithms for Automating 'Word of Mouth'", Conf. Human Factors in Computing Systems, 1995.

Cited by

  1. The Effect of the Personalized Settings for CF-Based Recommender Systems vol.18, pp.2, 2012, https://doi.org/10.13088/jiis.2012.18.2.131
  2. Improving Neighborhood-based CF Systems : Towards More Accurate and Diverse Recommendations vol.18, pp.3, 2011, https://doi.org/10.13088/jiis.2012.18.3.119
  3. K-Means Clustering 알고리즘과 헤도닉 모형을 활용한 서울시 연립·다세대 군집분류 방법에 관한 연구 vol.23, pp.3, 2017, https://doi.org/10.13088/jiis.2017.23.3.095