From Helpfulness Prediction to Helpful Review Retrieval for Online Product Reviews

Abstract

Nowadays, online product reviews belong to a valuable data source for customers in e-commerce. They provide customers with helpful details about a given product before customers make a decision on purchasing that product. Nevertheless, in this regard, if the e-commerce system returns too many reviews to customers and the reviews are not well presented in a relevant manner, the reviews might become cumbersome and time-consuming. In this paper, we define a helpful review retrieval task to support the customers by returning a ranked list of helpful reviews according to their helpfulness about the product of their interest. For an effective solution to the task, we also propose a method with an enhanced list of features for review representation and a multiple linear regression model using the elastic net regularization method. Our method is comprehensive as examining the task in its entirety from review's helpfulness prediction to helpful review retrieval for online product reviews. Evaluated on a real world Amazon dataset of the reviews about electronic devices, our method outperforms the others with the best values: 0.8 for the Normalized Discounted Cumulative Gain measure and 0.83 for the Accuracy measure. Such promising experimental results confirm the effectiveness of our method for the task.