Hybrid Collaborative Filtering Algorithm Based on Quality of Similarity
GUO Lei
ZHANG Kun
CHENG Hongyan
YAN Xia
Abstract：In the traditional collaborative filtering algorithm has been facing a cold start and data sparseness and other issues, resulting in the recommendation information is not accurate enough. A new hybrid collaborative filtering algorithm is proposed by an-alyzing the characteristics of user-based collaborative filtering algorithm and item-based collaborative filtering algorithm. This pa-per combines the weighted mean of two similar filtering algorithms with the mean and standard deviation of the similarity,and intro-duces the control factor to improve the precision of the prediction. Experiments are carried out with the Movie Lens dataset,and the average absolute error is used to measure the results. The experimental results show that the proposed algorithm improves the accura-cy of the proposed algorithm when the scoring matrix is extremely sparse.
Keywords：recommendation algorithmcollaborative filteringsimilarity
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2099-2104 )