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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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