[Objective] This paper designs a new deep learning algorithm to improve the recommendation results. [Methods] Our model evaluated user and item quality features from user ratings and item quality consistency, numerical distribution of ratings and time-period-based numerical distribution of ratings. [Results] We examined our model with the MovieLens dataset, and found the MAE and MSE were improved by up to 3.71% and 4.24%, respectively. [Limitations] More research is needed to explore a quality index evaluation method including attribute features of user and items. [Conclusions] The proposed model generates more accurate scoring prediction, and effectively improves the quality of recommendation.
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