DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박찬영 | - |
dc.contributor.author | Kim, Kibum | - |
dc.contributor.author | 김기범 | - |
dc.date.accessioned | 2024-07-25T19:31:07Z | - |
dc.date.available | 2024-07-25T19:31:07Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045863&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320640 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2023.8,[iii, 25 p. :] | - |
dc.description.abstract | The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed user and item problems exist at the same time, and considering only either one of them leads to sub-optimal performance of the other one. In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. MELT consists of bilateral branches each of which is responsible for long-tailed users and items, respectively, and the branches are trained to mutually enhance each other, which is trained effectively by a curriculum learning-based training. MELT is model-agnostic in that it can be seamlessly integrated with existing SRS models. Extensive experiments on eight datasets demonstrate the benefit of alleviating the long-tailed problems in terms of both users and items even without sacrificing the performance of head users and items, which has not been achieved by existing methods. To the best of our knowledge, MELT is the first work that jointly alleviates the long-tailed user and item problems in SRS. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 연속형 추천▼a긴꼬리 문제▼a전이 학습 | - |
dc.subject | Sequential Recommendation▼aLong-Tail Problem▼aTransfer Learning | - |
dc.title | MELT | - |
dc.title.alternative | 연속형 추천에서의 사용자 및 아이템 긴꼬리 동시 문제의 해결 방법론 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Park, Chanyoung | - |
dc.title.subtitle | mutual enhancement of long-tailed user and item for sequential recommendation | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.