注意力协同辅助变分推荐算法.

For the high dimension and insufficient sample of side information and user comment input in the recommendation algorithm, this paper proposed an attentive collaborative side variational autoencoder recommender model (sVAE-a) for the nonlinear modeling ability of variational autoencoder and the related data enhancement ability of the attention mechanism. This model used collaborative side variational autoencoder to model the side information. At the same time, it used the attention mechanism to integrate the side information into the collaborative variational autoencoder architecture to strengthen the hidden variables and provide the decoder with cleaner features. At last, it used the variational inference to approximate the side information and the user's comments, and obtained the recommended model through the parameters training. The experimental results on the MovieLens-20M dataset show that the method can improve not only from the basic recall rate index, but also from the coverage rate and normalized damage cumulative gain (NOCG) index. The model is easy to implement. It can combine different types of input and side information, and improve recommendation performance. [ABSTRACT FROM AUTHOR]