Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김상욱 | - |
dc.contributor.author | 박준하 | - |
dc.date.accessioned | 2021-02-24T16:00:48Z | - |
dc.date.available | 2021-02-24T16:00:48Z | - |
dc.date.issued | 2021. 2 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/158922 | - |
dc.identifier.uri | http://hanyang.dcollection.net/common/orgView/200000485634 | en_US |
dc.description.abstract | In this thesis, we start by pointing out the problem of a negative sampling (NS) strategy, denoted as nearest-NS, used in metric learning (ML)-based recommendation methods. The nearest-NS samples the items nearer to a user with higher probability among her unrated items. This could move her preferred items far away from her, thereby making the preferred items excluded from top-N recommendation. To address the problem, we first define a concept of a cage for a user, a region that contains the items highly likely preferred by her. Based on the concept, we propose a novel NS strategy, denoted as cage-based NS, that makes her preferred items rarely sampled as negative items. Through extensive experiments using four real-life datasets, we demonstrate that our cage-based NS strategy addresses successfully the problem of the nearest-NS strategy and outperforms consistently and significantly other NS strategies including the nearest-NS in terms of accuracy of recommendation. | - |
dc.publisher | 한양대학교 | - |
dc.title | Negative Sampling in Collaborative Metric Learning: Cage-based Approach | - |
dc.title.alternative | Collaborative Metric Learning을 위한 Cage 기반 네거티브 샘플링 | - |
dc.type | Theses | - |
dc.contributor.googleauthor | Junha Park | - |
dc.contributor.alternativeauthor | 박준하 | - |
dc.sector.campus | S | - |
dc.sector.daehak | 대학원 | - |
dc.sector.department | 컴퓨터·소프트웨어학과 | - |
dc.description.degree | Master | - |
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