An efficient and effective method to find uninteresting items for accurate collaborative filtering
- Authors
- Kim, Hyung-ook; Ha, Jiwoon; Kim, Sang-Wook
- Issue Date
- Feb-2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Collaborative Filtering; Data Imputation; Recommendation System; Zero-injection
- Citation
- 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, pp.3725 - 3730
- Indexed
- SCOPUS
- Journal Title
- 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
- Start Page
- 3725
- End Page
- 3730
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152926
- DOI
- 10.1109/SMC.2016.7844813
- 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.
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