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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 1425365, 12 pages
https://doi.org/10.1155/2018/1425365
Research Article

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

1School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
2Institute of Information Engineering, CAS, Beijing 100093, China

Correspondence should be addressed to Xu Yu; moc.361@2350uxuy and Jun-yu Lin; nc.ca.eii@uynujnil

Received 20 September 2017; Revised 6 January 2018; Accepted 16 January 2018; Published 12 February 2018

Academic Editor: Amparo Alonso-Betanzos

Copyright © 2018 Xu Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.