A Novel Collaborative Filtering Using Kernel Methods for Recommender Systems

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 at- tacks. Therefore, a novel CF using kernel methods for pre- diction 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 num- ber of iteration.