AspeRa: Aspect-based Rating Prediction Model
This work addresses the need for explainable rating predictions in recommender systems, though it is incremental in advancing aspect-based methods.
The authors tackled the problem of predicting user ratings from review texts while discovering coherent aspects for explanation, achieving significant performance improvements over state-of-the-art models like DeepCoNN and TransRev on two real-world datasets.
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.