We propose a novel Aspect-based Rating Prediction Model (AspeRa) for aspect-based representation learning for items by encoding word-occurrence statistics into word embeddings and applying dimensionality reduction to extract the most important aspects that are used for the user-item rating estimation. We investigate how and in what settings such neural autoencoders can be applied to contentbased recommendations for text items. ---------------------------------- **ASPERA MODEL** The AspeRa model combines the advantages of deep learning (end-to-end learning, spatial text representation) and topic modeling (interpretable topics) for text-based recommendation systems. Fig. 1 shows the overall architecture of AspeRa. The model receives as input two reviews at once, treating both identically. Each review is embedded with self-attention to produce two vectors, one for author (user) features and the other for item features. These two vectors are used to predict a rating corresponding to the review. All vectors are forced to belong to the same feature space. The embedding is produced by the Neural Attention-Based Aspect Extraction Model (ABAE) <cite>[7]</cite> .