- 성과
- 논문 (학술지) TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks
| 등록번호 | - |
SCI 구분
※구분 : SCI(SCIE포함), 비SCI
|
SCI |
|---|---|---|---|
| 저자명 (주·공동저자) | -; | ||
| 논문구분 | 국외전문학술지 | 학술지명 | Expert Systems with Applications |
| ISSN | 1873-6793 | 학술지 출판일자 | 2023-10-15 |
| 학술지 볼륨번호 | 228(n) | 논문페이지 | 1 ~ 13 |
| 학술지 임팩트팩터 | 8.665 | 기여율 | 70 % |
| DOI | https://doi.org/10.1016/j.eswa.2023.120487 | ||
- 초록
- Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.
☞ 성과발생연도 2022년 이후 논문은 조사분석 미확정 정보이며, 조사분석 확정시 기여율 등 일부 정보가 변경될 수 있습니다.
-
※ 연구개발성과 등록 또는 활용에 대한 문의는 논문 연구개발성과 담당자를 통해 문의하시기 바랍니다.
[문의] 한국과학기술정보연구원 Tel : 042)716-7066, https://curation.kisti.re.kr/
※ NTIS 관련 이용문의는 NTIS 콜센터(042-869-1115)로 문의하시기 바랍니다.
