  Multi-criteria recommender systems have been increasingly valuable for
helping consumers identify the most relevant items based on different
dimensions of user experiences. However, previously proposed multi-criteria
models did not take into account latent embeddings generated from user reviews,
which capture latent semantic relations between users and items. To address
these concerns, we utilize variational autoencoders to map user reviews into
latent embeddings, which are subsequently compressed into low-dimensional
discrete vectors. The resulting compressed vectors constitute latent
multi-criteria ratings that we use for the recommendation purposes via standard
multi-criteria recommendation methods. We show that the proposed latent
multi-criteria rating approach outperforms several baselines significantly and
consistently across different datasets and performance evaluation measures.
