Earticle

초록

영어

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

목차

Abstract
 1. 서론
 2. 연구 배경
  2.1 추천시스템
  2.2 협업필터링
  2.3 소셜 네트워크 분석과 연결 중심성
  2.4 신뢰 정보를 결합한 추천시스템
 3. 제안 알고리즘
 4. 실증분석
  4.1 실험 데이터
  4.2 실험 결과
 5. 결론
 References

키워드

저자

• 노희룡 [ Heeryong Noh | Ph.D. Candidate, Graduate School of Business IT, Kookmin University ]
• 안현철 [ Hyunchul Ahn | Associate Professor, Graduate School of Business IT, Kookmin University ] Corresponding author

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