Before revision: Supporting recommendations with personalized and relevant explanations increases trust and perceived quality , and helps users make better decisions. Prior work attempted to generate a synthetic review or review segment as an explanation, but they were not judged convincing in evaluations by human users . We propose T-RECS, a multi-task learning Transformer-based model that jointly performs recommendation with textual explanations using a novel multi-aspect masking technique . We show that human users significantly prefer the justifications generated by T-RECS than those generated by state-of-the-art techniques.
After revision: Supporting recommendations with personalized and relevant explanations increases trust and perceived quality . However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer the justifications generated by T-RECS than those generated by state-of-the-art techniques.