Review Helpfulness Prediction with Embedding-Gated CNN
This work addresses the problem of presenting helpful reviews to consumers for e-commerce companies, but it appears incremental as it builds on existing CNN and transfer learning approaches.
The authors tackled the problem of predicting review helpfulness for e-commerce by introducing a convolutional neural network model with word-level embedding-gates and cross-domain knowledge transfer, which achieved better performance than existing methods in experiments.
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.