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
- Author
- 김상욱
- Keywords
- Recommendation System; Collaborative Filtering; Data Imputation; Zero-injection
- Issue Date
- 2016-10
- Publisher
- IEEE SMC 2016
- Citation
- 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Page. 3725-3731
- 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
- https://ieeexplore.ieee.org/document/7844813?arnumber=7844813&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/98956
- ISBN
- 978-1-5090-1897-0
- DOI
- 10.1109/SMC.2016.7844813
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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