2019/01/23 by Sergey Nikolenko, Nikolenko, Sergey I., Elena Tutubalina +7
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.1901.07829
openalex publication_date 2019/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.