Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김상욱 | - |
dc.contributor.author | Kim, Hyung-ook | - |
dc.date.accessioned | 2017-11-29T02:30:21Z | - |
dc.date.available | 2017-11-29T02:30:21Z | - |
dc.date.issued | 2017-08 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/33666 | - |
dc.identifier.uri | http://hanyang.dcollection.net/common/orgView/200000430859 | en_US |
dc.description.abstract | Collaborative filtering methods suffer from a data sparsity problem, which indicates that the accuracy of recommendation decreases when the user-item matrix used in recommendation is sparse. To alleviate the data sparsity problem, researches on data imputation have been done. In particular, the zero-injection method, which finds uninteresting items and imputes zero values to those items for collaborative filtering, achieves significant improvement in terms of recommendation accuracy. However, the existing zero-injection method employs the One-Class Collaborative Filtering (OCCF) method that requires a lot of time. In this paper, we propose a fast method that finds uninteresting items rapidly with preserving high recommendation accuracy. Our experimental results show that our method is faster than the existing zero-injection method and also show that the recommendation accuracy using our method is slightly higher than or similar to that of the existing zero-injection method. | - |
dc.publisher | 한양대학교 | - |
dc.title | An Efficient and Effective Method to Find Uninteresting Items for Accurate Collaborative Filtering | - |
dc.title.alternative | 정확한 협업 필터링을 위해 무관심 아이템을 찾는 효율적이고 효과적인 방법 | - |
dc.type | Theses | - |
dc.contributor.googleauthor | 김형욱 | - |
dc.contributor.alternativeauthor | 김형욱 | - |
dc.sector.campus | S | - |
dc.sector.daehak | 대학원 | - |
dc.sector.department | 컴퓨터·소프트웨어학과 | - |
dc.description.degree | Master | - |
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