User Bias Removal in Review Score Prediction
This work addresses noise reduction in recommendation systems for e-commerce, but it is incremental as it builds on existing methods with minor modifications.
The paper tackled the problem of user bias in review score prediction by proposing two simple statistical methods to remove noise, resulting in improved prediction across three text feature representations on Amazon review datasets.
Review score prediction of text reviews has recently gained a lot of attention in recommendation systems. A major problem in models for review score prediction is the presence of noise due to user-bias in review scores. We propose two simple statistical methods to remove such noise and improve review score prediction. Compared to other methods that use multiple classifiers, one for each user, our model uses a single global classifier to predict review scores. We empirically evaluate our methods on two major categories (\textit{Electronics} and \textit{Movies and TV}) of the SNAP published Amazon e-Commerce Reviews data-set and Amazon \textit{Fine Food} reviews data-set. We obtain improved review score prediction for three commonly used text feature representations.