Review Helpfulness Prediction with Embedding-Gated CNN
Abstract
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions. Thus, it is important for e-commerce companies to predict review helpfulness to present and recommend reviews in a more informative manner. In this work, we introduce a convolutional neural network model that is able to extract abstract features from multi-granularity representations. Inspired by the fact that different words contribute to the meaning of a sentence differently, we consider to learn word-level embedding-gates for all the representations. Furthermore, as it is common that some product domains/categories have rich user reviews, other domains not. To help domains with less sufficient data, we integrate our model into a cross-domain relationship learning framework for effectively transferring knowledge from other domains. Extensive experiments show that our model yields better performance than the existing methods.
Cite
@article{arxiv.1808.09896,
title = {Review Helpfulness Prediction with Embedding-Gated CNN},
author = {Cen Chen and Minghui Qiu and Yinfei Yang and Jun Zhou and Jun Huang and Xiaolong Li and Forrest Bao},
journal= {arXiv preprint arXiv:1808.09896},
year = {2018}
}