User Bias Removal in Review Score Prediction

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 ($\mathit{\text{Electronics}}$ and $\mathit{\text{Movies}}\mathit{\text{and}}\mathit{\text{TV}}$) of the SNAP published Amazon e-Commerce Reviews data-set and Amazon $\mathit{\text{Fine}}\mathit{\text{Food}}$ reviews data-set. We obtain improved review score prediction for three commonly used text feature representations.