摘要：

Abstract:

【Purpose/significance】This study tries to improve the limitation of matrix factorization and optimize the effect of book per⁃

sonalized recommendation based on deep metric factorization.【Method/process】First, we convert the rating matrix into distance met⁃

ric matrix. Then, calculating the distance between user and item based on Metric Factorization method. Finally, we use deep learning

framework to build this model.【Result/conclusion】We use Douban book rating dataset to train and test this model, meanwhile we com⁃

pare our approach with three baselines. The results show that our approach is better than other baselines on RMSE and MAE. And the

experiment results show that this approach can improve the effect of book personalized recommendation.【Innovation/limitation】The

effect of book personalized recommendation has been optimized based on deep metric factorization. But this method is only experiment⁃

ed on a real dataset, and it needs to be tested on more dataset in the future.