Graph-theoretic one-class collaborative filtering using signed random walk with restart
- Authors
- Lee, Yeon-Chang; Park, Daeyoung; Son, Jiwon; Kim, Taeho; Kim, Sang-Wook
- Issue Date
- Feb-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Graph theory; One-class collaborative filtering; Random walk with restart
- Citation
- Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, pp.98 - 101
- Indexed
- SCOPUS
- Journal Title
- Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
- Start Page
- 98
- End Page
- 101
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146194
- DOI
- 10.1109/BigComp48618.2020.00-93
- Abstract
- Graph-theoretic one-class collaborative filtering (gOCCF) has been successful in dealing with sparse datasets in one-class setting (e.g., clicked or bookmarked). In this paper, we point out the problem that gOCCF requires long processing time compared to existing OCCF methods. To overcome the limitation of the original gOCCF, we propose a new gOCCF method based on signed random walk with restart (SRWR). Using SRWR, the proposed method accurately and efficiently captures users' preferences by analyzing not only positive preferences from rated items but also the negative preferences from uninteresting items. Through extensive experiments using real-life datasets, we verify that the proposed method improves the accuracy of the original gOCCF and requires processing time less than the original gOCCF.
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