A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems  

作　　者：CAO Jie WU Zhiang ZHUANG Yi MAO Bo YU Zeng 

机构地区：[1]Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing 210003, China [2]College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China

出　　处：《Chinese Journal of Electronics》2012年第4期609-614,共6页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.71072172, No.61103229, No.60003047）, Industry Projects in the Jiangsu Science ＆ Technology Pillar Program （No.BE2011198）, Jiangsu Provincial Key Laboratory of Network and Information Security （Southeast University） （No.BM2003201） and the Program of Natural Science Foundation of Zhejiang Province （No.Y1090165, No.Yll10644, No.Yll10969）.

摘　　要：Recommender systems form an essential part of e-business systems. Collaborative filtering （CF）, a widely used technique by recommender systems, performs poorly for cold start users and is vulnerable to shilling attacks. Therefore, a novel CF using kernel methods for prediction is proposed. The method is called Iterative kernelbased CF （IKCF）, for it is an iterative process. First, mode or mean is used to smooth the unknown ratings; second, discrete or continuous kernel estimators are used to generate predicted ratings iteratively and to export the predicted ratings in the end. The experimental results on three real-world datasets show that, with IKCF as a booster, the prediction accuracy of recommenders can be significantly improved especially for sparse datasets. IKCF can also achieve high prediction accuracy with a small number of iteration.

关 键 词：Recommender systems Collaborative fil- tering Kernel methods Prediction. 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] TP311[自动化与计算机技术—计算机科学与技术]