Collaborative filtering recommendation algorithm using item category information
Zhao Xue-bin · Journal of Chongqing University of Posts and Telecommunications · 2010
Aiming at the difficulty of data sparsity and inaccurate user similarity in personalized recommendation systems,a new algorithm of collaborative filtering using item category information was proposed.The algorithm used user rating data to calculate category concern similarity between users.Category concern similarity and user rating similarity had been synthesized to get synthetic user similarity,thus the accurate degree of searching nearest neighbor users has been improved and the sparse of rating data problem has been alleviated simultaneously.The experiment shows that the measure can avoid the defects of traditional similarity measure and reduce the negative effect on the final recommendation and provide better reco-mmendation results for the system.