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Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviewsbased on predicted helpfulness scores and has been widely applied in e-commercevia presenting customers with useful reviews. Previous studies commonly employfully-connected neural networks (FCNNs) as the final score predictor andpairwise loss as the training objective. However, FCNNs have been shown toperform inefficient splitting for review features, making the model difficultto clearly differentiate helpful from unhelpful reviews. Furthermore, pairwiseobjective, which works on review pairs, may not completely capture the MRHPgoal to produce the ranking for the entire review list, and possibly induceslow generalization during testing. To address these issues, we propose alistwise attention network that clearly captures the MRHP ranking context and alistwise optimization objective that enhances model generalization. We furtherpropose gradient-boosted decision tree as the score predictor to efficaciouslypartition product reviews' representations. Extensive experiments demonstratethat our method achieves state-of-the-art results and polished generalizationperformance on two large-scale MRHP benchmark datasets.

Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction