Utilizing a feature-aware external memory network for helpfulness prediction in e-commerce reviews

With the explosive growth of reviews on e-commerce websites, consumers usually have trouble finding helpful information in massive reviews. Therefore, predicting review helpfulness has drawn the attention of researchers in the research community. Previous works have focused on identifying the helpfulness of reviews based on content, but we found that the helpfulness of a review is determined by the content of reviews and the matching degree between items and reviews. To meet this challenge, this paper proposes a deep matching model based on a feature-aware external memory network (DMMN) to predict review helpfulness. Specifically, the DMMN contains a memory matrix that can store typical features of items via memory reading/writing operations. We then derive item features by properly reading the memory matrix. Meanwhile, a self-attention mechanism is adopted to obtain review features. Finally, the helpfulness score of the review is acquired by the matching degree between item features and review features. Experiments conducted on Amazon datasets, compared with the baseline models, the DMMN obtains an about 1–2% performance improvement on the eight datasets. The experimental results demonstrate that our efforts to predict helpful reviews are feasible and effective. •Review helpfulness prediction is treated as a matching process, considering review content versatility and item consistency.•An external memory network is introduced to extract and store features of items from reviews.•A deep matching model based on feature-aware external memory network is proposed to perform review helpfulness prediction.