An Efficient and Effective Method to Find Uninteresting Items for Accurate Collaborative Filtering
- Title
- An Efficient and Effective Method to Find Uninteresting Items for Accurate Collaborative Filtering
- Other Titles
- 정확한 협업 필터링을 위해 무관심 아이템을 찾는 효율적이고 효과적인 방법
- Author
- Kim, Hyung-ook
- Alternative Author(s)
- 김형욱
- Advisor(s)
- 김상욱
- Issue Date
- 2017-08
- Publisher
- 한양대학교
- Degree
- Master
- 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.
- URI
- http://hdl.handle.net/20.500.11754/33666http://hanyang.dcollection.net/common/orgView/200000430859
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > COMPUTER SCIENCE(컴퓨터·소프트웨어학과) > Theses (Ph.D.)
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