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A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression
Joint Authors
Yu, Xu
Lin, Jun-yu
Jiang, Feng
Du, Jun-wei
Han, Ji-zhong
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains.
Obviously, different auxiliary domains have different importance to the target domain.
However, previous works cannot evaluate effectively the significance of different auxiliary domains.
To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR).
We first construct features in different domains and use these features to represent different auxiliary domains.
Thus the weight computation across different domains can be converted as the weight computation across different features.
Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem.
Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem.
As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods.
We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.
American Psychological Association (APA)
Yu, Xu& Lin, Jun-yu& Jiang, Feng& Du, Jun-wei& Han, Ji-zhong. 2018. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
Modern Language Association (MLA)
Yu, Xu…[et al.]. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
American Medical Association (AMA)
Yu, Xu& Lin, Jun-yu& Jiang, Feng& Du, Jun-wei& Han, Ji-zhong. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130590
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130590