A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems

作者：Cao Jie; Wu Zhiang^*; Zhuang Yi; Mao Bo; Yu Zeng

来源：Chinese Journal of Electronics, 2012, 21(4): 609-614.

• Recommender systems
• Collaborative filtering
• Kernel methods
• Prediction

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 kernel-based 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.

• 出版日期2012-10
• 单位南京财经大学; 浙江工商大学