Exploiting matrix factorization to asymmetric user similarities in recommendation systems
- Authors
- Pirasteh, Parivash; Hwang, Dosam; Jung, Jason J.
- Issue Date
- Jul-2015
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Collaborative filtering; Matrix factorization; Recommender systems; User similarity; Asymmetry
- Citation
- KNOWLEDGE-BASED SYSTEMS, v.83, no.1, pp 51 - 57
- Pages
- 7
- Journal Title
- KNOWLEDGE-BASED SYSTEMS
- Volume
- 83
- Number
- 1
- Start Page
- 51
- End Page
- 57
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9369
- DOI
- 10.1016/j.knosys.2015.03.006
- ISSN
- 0950-7051
1872-7409
- Abstract
- Although collaborative filtering is widely applied in recommendation systems, it still suffers from several major limitations, including data sparsity and scalability. Sparse data affects the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure aimed at providing a valid similarity measurement between users with very few ratings. The contributions of this paper are twofold: First, we suggest an asymmetric user similarity method to distinguish between the impact that the user has on his neighbor and the impact that the user receives from his neighbor. Second, we apply matrix factorization to the user similarity matrix in order to discover the similarities between users who have rated different items. Experimental results show that our method performs better than commonly used approaches, especially under cold-start condition. (C) 2015 Elsevier B.V. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9369)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.