Seq2CASE: Weakly Supervised Sequence to Commentary Aspect Score Estimation for Recommendation

Abstract

Online users’ feedback has numerous text comments to enrich the review quality on mainstream platforms, such as Yelp and Google Maps. Reading through numerous review comments to speculate the important aspects is tedious and time-consuming. Apparently, there is a huge gap between the numerous commentary text and the crucial aspects for users’ preferences. In this study, we proposed a weakly supervised framework called Sequence to Commentary Aspect Score Estimation (Seq2CASE) to estimate the vital aspect scores from the review comments, since the ground truth of the aspect score is seldom available. The aspect score estimation from Seq2CASE is close to the actual aspect scoring; precisely, the average Mean Absolute Error (MAE) is less than 0.4 for a 5-point grading scale. The performance of Seq2CASE is comparable to or even better than the state-of-the-art supervised approaches in recommendation tasks. We expect this work to be a stepping stone that can inspire more unsupervised studies working on this important but relatively underexploited research.