Earticle

초록

영어

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

목차

Abstract
 1. 서론
 2. 연구 배경
  2.1 기존 추천 기법의 한계점
  2.2 차원축소 관련연구
 3. 유전자 알고리즘 기반의 협동필터링 차원축소 모형
 4. 실험 설계
  4.1 실험데이터 : MovieLens 데이터셋
  4.2 실험 설계
 5. 실험 결과
 6. 결언
 참고문헌

키워드

저자

• 김경재 [ Kyoung-jae Kim | 동국대학교 경영대학 경영정보학과 부교수 ] 교신저자
• 안현철 [ Hyunchul Ahn | 국민대학교 경상대학 비즈니스IT학부 전임강사 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005