The performance of the recommendation systems is restricted by data sparsity， and the accuracy of prediction can be effectively improved by reasonably filling the missing values in the rating matrix. Therefore， a new algorithm named Recommendation Rating Prediction based on Concept Lattice Reduction （RRP-CLR） was proposed. RRP-CLR algorithm was composed of nearest neighbor selection module and rating prediction module， which were respectively responsible for generating reduced nearest neighbor set and realizing rating prediction and recommendation. In the nearest neighbor selection module， the user rating matrix was transformed into a binary matrix， which was regarded as the user interest formal background. Then the formal background reduction rules and concept lattice redundancy concept deletion rules were proposed to improve the efficiency of generating reduced nearest neighbors. In the rating prediction module， a new user similarity calculation method was proposed to eliminate the impact of rating deviations caused by user’s subjective factors on similarity calculation. When the number of common rating items of two users was less than a specific threshold， the similarity was scaled appropriately to make the similarity between users more consistent with the real situation. Experimental results show that compared with PC?UCF （User-based Collaborative Filtering recommendation algorithm based on Pearson Coefficient） and RRP-UICL （Recommendation Rating Prediction method based on User Interest Concept Lattice）， RRP-CLR algorithm has smaller Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE）， and better rating prediction accuracy and stability.