  Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews
based on predicted helpfulness scores and has been widely applied in e-commerce
via presenting customers with useful reviews. Previous studies commonly employ
fully-connected neural networks (FCNNs) as the final score predictor and
pairwise loss as the training objective. However, FCNNs have been shown to
perform inefficient splitting for review features, making the model difficult
to clearly differentiate helpful from unhelpful reviews. Furthermore, pairwise
objective, which works on review pairs, may not completely capture the MRHP
goal to produce the ranking for the entire review list, and possibly induces
low generalization during testing. To address these issues, we propose a
listwise attention network that clearly captures the MRHP ranking context and a
listwise optimization objective that enhances model generalization. We further
propose gradient-boosted decision tree as the score predictor to efficaciously
partition product reviews' representations. Extensive experiments demonstrate
that our method achieves state-of-the-art results and polished generalization
performance on two large-scale MRHP benchmark datasets.
