scholarly journals A User-Based Cross Domain Collaborative Filtering Algorithm Based on a Linear Decomposition Model

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 27582-27589 ◽  
Author(s):  
Xu Yu ◽  
Feng Jiang ◽  
Junwei Du ◽  
Dunwei Gong
Keyword(s):  
Collaborative Filtering ◽  
Filtering Algorithm ◽  
Decomposition Model ◽  
Cross Domain ◽  
Collaborative Filtering Algorithm

scholarly journals A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Yu ◽  
Jun-yu Lin ◽  
Feng Jiang ◽  
Jun-wei Du ◽  
Ji-zhong Han
Keyword(s):  
Linear Regression ◽  
Collaborative Filtering ◽  
Feature Construction ◽  
Target Domain ◽  
Regression Problem ◽  
Filtering Algorithm ◽  
Weighted Linear Regression ◽  
Cross Domain ◽  
Collaborative Filtering Algorithm ◽  
Sparsity Problem

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.

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