Negative Sampling in Collaborative Metric Learning: Cage-based Approach
- Title
- Negative Sampling in Collaborative Metric Learning: Cage-based Approach
- Other Titles
- Collaborative Metric Learning을 위한 Cage 기반 네거티브 샘플링
- Author
- 박준하
- Alternative Author(s)
- 박준하
- Advisor(s)
- 김상욱
- Issue Date
- 2021. 2
- Publisher
- 한양대학교
- Degree
- Master
- 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.
- URI
- https://repository.hanyang.ac.kr/handle/20.500.11754/158922http://hanyang.dcollection.net/common/orgView/200000485634
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > COMPUTER SCIENCE(컴퓨터·소프트웨어학과) > Theses (Master)
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