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.

翻译：当前，矩阵分解通过因子分解有效处理大规模评分矩阵，是最广泛使用的协同过滤算法之一。该算法主要利用用户与项目间的交互记录来预测评分。本文基于用户与项目的特征属性，提出一种将电影类型属性和用户年龄属性融合至SVD++框架的新型UISVD++模型。通过将年龄属性投影至用户隐空间、类型属性投影至项目隐空间，该模型丰富了用户和项目的辅助信息。最后，我们在Movielens-100K和Movielens-1M两个公开数据集上进行对比实验。实验结果表明，在评分预测任务中，该模型的预测精度优于其他基线模型。此外，这些结果还表明UISVD++能有效缓解冷启动问题。