Abstract：The existing microblog user-tag recommendation methods mostly rely on friends relationship or content to realize recommendation, and the bandwagon relationship existing in microblog (noise problem) can not be discovered and the user label sparse problem is not solved. Therefore, a microblog user-tag recommendation algorithm based on noise reduction relation regularization is presented. The similarity of the user′s and his friends′ interests is measured by the micro-blog theme of users extracted by LDA to reduce the influence of those friends without interests in common with the target user. The noise reduction relationship is taken as the regularization item and it is introduced into user-tag nonnegative matrix factorization model to solve the user-tag sparse problem. The model is optimized and constrained via the Lagrange multiplier method and the KKT conditions, and finally the approximate user-tag matrix for recommended users′ tag is obtained. The experimental results show the proposed method exposes the high quality in recommendation.
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2017, Vol. 30 
