Best Approximate Distribution-based Model for Helpful Vote of Customer Review Prediction

Product reviews are more and more important for potential customers to decide on their purchases in electronic commerce nowadays. The helpful vote is a critical indicator of how much impact the review has on other customers. Therefore, the prediction of helpful votes is an essential task. Linear and Tobit Regression are general methods of the prediction. Those methods share the same objective function and come from the initial assumption that the helpful votes on any dataset follow a normal distribution. However, the assumption is not usually confirmed, and the distribution of the helpful votes often follows other distributions. Consequently, the prediction results might not be fully appropriate. This paper proposes a model that follows the best approximate distribution of helpful votes to predict the number of helpful votes. On top of that, considering the elapsed time since reviews were written, we propose an adaptive window size sampling method to evaluate the model on review datasets sorted chronologically. To validate the proposed model, we conducted extensive experiments on real-world datasets. Experimental results illustrate the validity of the proposed model.