  Currently, matrix decomposition is one of the most widely used collaborative
filtering algorithms by using factor decomposition to effectively deal with
large-scale rating matrix. It mainly uses the interaction records between users
and items to predict ratings. Based on the characteristic attributes of items
and users, this paper proposes a new UISVD++ model that fuses the type
attributes of movies and the age attributes of users into SVD++ framework. By
projecting the age attribute into the user's implicit space and the type
attribute into the item's implicit space, the model enriches the side
information of the users and items. At last, we conduct comparative experiments
on two public data sets, Movielens-100K and Movielens-1M. Experiment results
express that the prediction accuracy of this model is better than other
baselines in the task of predicting scores. In addition, these results also
show that UISVD++ can effectively alleviate the cold start situation.
