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      KCI등재

      협력적 여과 시스템에서 산포도를 이용한 잡음 감소 = Reducing Noise Using Degree of Scattering in Collaborative Filtering System

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      https://www.riss.kr/link?id=A103973861

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      다국어 초록 (Multilingual Abstract)

      Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.
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      Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevan...

      Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.

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      참고문헌 (Reference)

      1 Sarwar B. M, "Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System" 1998.

      2 Wang J, "Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion" 2006.

      3 Massa P, "Trust-aware Collaborative Filtering for Recommender Systems" 2004.

      4 Spiegel Murray R, "Schaum's Outline of Statistics" McGraw-Hill 1998.

      5 Basu C, "Recommendation as classification:Using social and content-based information in recommendation" 1998.

      6 "MovieLens collaborative filtering data set" 2000.

      7 Robu V, "Learning the Structure of Utility Graphs Used in Multi-Issue Negotiation through Collaborative Filtering" 2005.

      8 Sarwar B. M, "Item-based Collaborative Filtering Recommendation Algorithms" 2001.

      9 Salton, "Introduction to Modern Information Retrieval" McGraw-Hill 1983.

      10 Konstan J, "GroupLens:Applying Collaborative Filtering to Usenet News" 40 (40): 77-87, 1997.

      1 Sarwar B. M, "Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System" 1998.

      2 Wang J, "Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion" 2006.

      3 Massa P, "Trust-aware Collaborative Filtering for Recommender Systems" 2004.

      4 Spiegel Murray R, "Schaum's Outline of Statistics" McGraw-Hill 1998.

      5 Basu C, "Recommendation as classification:Using social and content-based information in recommendation" 1998.

      6 "MovieLens collaborative filtering data set" 2000.

      7 Robu V, "Learning the Structure of Utility Graphs Used in Multi-Issue Negotiation through Collaborative Filtering" 2005.

      8 Sarwar B. M, "Item-based Collaborative Filtering Recommendation Algorithms" 2001.

      9 Salton, "Introduction to Modern Information Retrieval" McGraw-Hill 1983.

      10 Konstan J, "GroupLens:Applying Collaborative Filtering to Usenet News" 40 (40): 77-87, 1997.

      11 Schein A. I, "Generate Models for Cold-start Recommendations" 2001.

      12 Herlocker J, "Evaluating Collaborative Filtering Recommender Systems" 22 (22): 2004.

      13 Breese John. S, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering" Madison, WI 1998.

      14 Lee W. S, "Collaborative learning for recommender systems" 1997.

      15 Sarwar B. M, "Application of Dimensionality Reduction in Recommender System-A Case Study" 2000.

      16 Herlocker J, "An Algorithmic Framework for Performing Collaborative Filtering" 1999

      17 Mui L, "A Probabilistic Model for Collaborative Sanctioning" 617 : 2001.

      18 Reddy P. K, "A Graph based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering" Springer-Verlag 2002.

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2012-10-31 학술지명변경 한글명 : 소프트웨어 및 데이터 공학 -> 정보처리학회논문지. 소프트웨어 및 데이터 공학 KCI등재
      2012-10-10 학술지명변경 한글명 : 정보처리학회논문지B -> 소프트웨어 및 데이터 공학
      외국어명 : The KIPS Transactions : Part B -> KIPS Transactions on Software and Data Engineering
      KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2003-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2002-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2000-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.35 0.35 0.28
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.23 0.19 0.511 0.06
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