Collaborative Filtering （CF） algorithm can realize personalized recommendation on the basis of the similarity between items or users. However， data sparsity has always been one of the challenges faced by CF algorithm. In order to improve the prediction accuracy， a CF algorithm based on Collaborative Training and Boosting （CFCTB） was proposed to solve the problem of sparse user-item scores. First， two CFs were integrated into a framework by using collaborative training， pseudo-labeled samples with high confidence were added to each other’s training set by the two CFs， and Boosting weighted training data were used to assist the collaborative training. Then， the weighted integration was used to predict the final user scores， and the accumulation of noise generated by pseudo-labeled samples was avoided effectively， thereby further improving the recommendation performance. Experimental results show that the accuracy of the proposed algorithm is better than that of the single models on four open datasets. On CiaoDVD dataset with the highest sparsity， compared with Global and Local Kernels for recommender systems （GLocal-K）， the proposed algorithm has the Mean Absolute Error （MAE） reduced by 4.737%. Compared with ECoRec （Ensemble of Co-trained Recommenders） algorithm， the proposed algorithm has the Root Mean Squared Error （RMSE） decreased by 7.421%. The above rasults verify the effectiveness of the proposed algorithm.