Robust collaborative filtering recommendation algorithm incorporated with the difference of individual features
YI Hua-wei
ZHU Wei
Abstract：The existing recommendation algorithms have poor robustness against shilling attacks.In this con-sideration,in this paper we propose a robust recommendation algorithm incorporated with the difference of indi-vidual features.We first give two individual features which are user′s deviation degree of rating numbers and av-erage similarity of user′s neighbors.According to the distribution of users′ratings,we introduce the computation-al methods of individual features.Then we give the algorithm which can be used to label suspicious users based on the differences of computational results of individual features.Finally,we incorporate the matrix factorization technology with the identification results of suspicious users to make recommendations for users.Experimental re-sults show that the proposed algorithm not only improves the recommendation accuracy, but also has better ro-bustness.
Keywords：robust collaborative filteringshilling attacksmatrix factorizationindividual featuressuspi-cious users
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 256-263 )