사용자 간 신뢰․불신 관계 네트워크 분석 기반 추천 알고리즘에 관한 연구
A Study on the Recommendation Algorithm based on Trust/Distrust Relationship Network Analysis
- 발행
- 2017 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 국민대학교
- 출처
- 국내 KCI
- DOI
- 10.21219/jitam.2017.24.1.169
- 원문
- 원문 보기 ↗
개념
키워드
Recommender System, Collaborative Filtering, Trust, Distrust, Social Network Analysis, Recommender System, Collaborative Filtering, Trust, Distrust, Social Network Analysis
초록
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 positi