Exploiting matrix factorization to asymmetric user similarities in recommendation systems

Title
Exploiting matrix factorization to asymmetric user similarities in recommendation systems
Author(s)
황도삼파리바쉬피라스데정재은[정재은]
Keywords
COLLABORATIVE RECOMMENDATION; ALLEVIATE; IMPROVE
Issue Date
201507
Publisher
ELSEVIER SCIENCE BV
Citation
KNOWLEDGE-BASED SYSTEMS, v.83, pp.51 - 57
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.
URI
http://hdl.handle.net/YU.REPOSITORY/31576http://dx.doi.org/10.1016/j.knosys.2015.03.006
ISSN
0950-7051
Appears in Collections:
공과대학 > 컴퓨터공학과 > Articles
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