  We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa)
that estimates user rating based on review texts for the items and at the same
time discovers coherent aspects of reviews that can be used to explain
predictions or profile users. The AspeRa model uses max-margin losses for joint
item and user embedding learning and a dual-headed architecture; it
significantly outperforms recently proposed state-of-the-art models such as
DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews.
With qualitative examination of the aspects and quantitative evaluation of
rating prediction models based on these aspects, we show how aspect embeddings
can be used in a recommender system.
