A Collaborative Filtering Recommender Algorithm based on Iterative Kernel Method
LIU Li
Abstract：Collaborative filtering is an effective method in recommender system. However, while dealing with cold start users and sparse rating matrix, the performance of collaborative filtering decreases quickly. In order to improve the ability of handle cold start users and sparse rating matrix, this paper proposes an iterative collaborative filtering recommender algorithm based on kernel method. Firstly, builds a data cube according to the user-item rating matrixes, defines three kernel functions for continuous value, ordering discrete values and non-ordering discrete values, designs three kernel estimators based on these kernel functions, and finally, predicts unknown ratings with the proposed kernel estimators. The experiments show that, compared with user based and item based collaborative filtering methods, the proposed algorithm has less predicting error, and can handle cold start users and sparse rating matrix more effectively.
Keywords：Collaborative filteringKernel methodRecommender SystemNearest neighbor
Publication Date：2014-01-01
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：6( 76-81 )