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
Currently, Music Recommendation (MR) platforms are involved in exploiting meaning-based representations of music data to deliver personalized recommendations. These models enhance music discovery by analyzing user preferences, song attributes, and contextual data. However, the existing frameworks heavily depend on static user-item interactions and knowledge graphs, which leads to overlooking dynamic factors that reduce adaptivity in real-world entertainment platforms. Hence, this study proposes a Context-enriched Temporal Knowledge Graph with Reinforcement Learning (CTKG-RL) for adaptive MR in entertainment platforms. Initially, the input data were collected from the Last.FM_ 1 K dataset, which contains user-track interactions and then preprocessed with timestamp conversion, Levenshtein string matching, and thresholding-based filtering. This process ensures that the model has consistent time alignment and deduplication of track metadata and retains only active users and items, respectively. Further, a TKG is constructed from semantic and contextual triples, and then the embeddings are learned using Temporal Graph Attention Networks (TGANs) to capture evolving user interests. Subsequently, session modeling with a Graph Recurrent Unit (GRU)-based encoder aggregates short- and long-term preferences, and finally, an actor-critic RL model optimizes adaptive playlist generation. The proposed CTKG-RL achieved better results in terms of accuracy (0.893) when compared to the existing Multi-channel and Multi-loss MR Knowledge Graph (MM-MRKG) model.
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.
최근 본 자료 전체보기
