인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Cold-start recommendation helps personalize user experiences and improve the relevance of the recommendation system. Despite its importance, cold-start solutions are difficult to develop because new users and items lack interaction data, making user preferences and item relevance prediction difficult. Cold-start recommendation systems face challenges because new users/items often lack historical data, resulting in suboptimal recommendation performance. This study presents the cold-start recommendation network (CSRNet) model to address the critical issue of inadequate data in new user and item recommendations, a common limitation in traditional recommendation systems. Our novel approach uses advanced machine learning techniques to create a dynamic model that adapts to no historical interaction data. Hierarchical density-based clustering groups’ subtle similarities improve recommendation accuracy when combined with transfer learning’s predictive power and Bi-GRU’s sequential data handling. Synergistic techniques optimize cold-start recommendations in this integration. By methodically overcoming data sparsity and improving recommendation quality without historical data, CSRNet sets a new standard for adaptive, accurate, and efficient recommendation systems. We tested the proposed method on a pre-processed, well-defined dataset, dividing it into training and test sets to ensure data quality and model resilience. We evaluated the CSRNet model using accuracy, precision, recall, F 1-score, Root Mean Square Error (RMSE), and Mean Absolute Error. Our model has 90.90% accuracy, 90.2% precision, 90.9% recall, 90.9% F 1-score, 1.0059 RMSE, and 0.8012 MAE, outperforming leading cold-start recommendation approaches. This shows that our model can handle the complexities of cold-start recommendation and apply it across domains to provide personalized and relevant recommendations without extensive historical interaction data.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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