Noise reduction auto-encoder recommendation method based on attention model

The invention discloses a noise reduction auto-encoder movie recommendation method based on an attention model, and belongs to the technical field of movie recommendation. In an existing recommendation algorithm, an auto-encoder recommendation model is easy to realize and widely apply due to high operation speed, but when a scoring matrix is sparse, the recommendation accuracy is greatly reduced,and different attention degrees of auxiliary information and a user to watching records are not considered. In order to solve the above problem, the method combines an attention model with a noise reduction auto-encoder, learns the preference of a user by using the attention model, and integrates the preference into the noise reduction auto-encoder to jointly iterate and update parameters, therebypredicting the complete score of the user. According to the method, the prediction scoring accuracy is obviously improved. 一种基于注意力模型的降噪自编码器的电影推荐方法，属于电影推荐技术领域。在现有的推荐算法中，自编码器推荐模型因运算速度较快，易于实现得到广泛应用，但评分矩阵稀疏时，推荐准确度将大大降低，且未曾考虑辅助信息与