%0 Journal Article
%A CHEN De-ren
%A DING Wei-feng
%A ZHENG Xiao-lin
%T Active Sampling Based on PureSVD Model for Collaborative Filtering
%D 2013
%R 10.13190/jbupt.201304.21.047
%J Journal of Beijing University of Posts and Telecommunications
%P 23-26
%V 36
%N 4
%X <p>A parameter-change maximization sampling method is proposed to capture new user's preference in recommender system. This method produces an item list that maximizes model parameter change based on pure singular value decomposition (PureSVD). By querying new user with specific item list, the ratings are obtained for training the corresponding user's parameter in PureSVD model, it performs prediction for new users in return. A greedy approximation algorithm is presented to produce the item list with an acceptable time bound. Experiments show that the method can learn new user's preference efficiently with small sample size under Top-N metrics.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/jbupt.201304.21.047