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.

翻译：多模态评论有用性预测（MRHP）旨在根据预测的有用性评分对产品评论进行排序，并通过向客户展示有用的评论广泛应用于电子商务。以往研究通常采用全连接神经网络（FCNNs）作为最终评分预测器，并以成对损失作为训练目标。然而，研究表明FCNNs对评论特征的划分效率低下，导致模型难以清晰区分有用与无用评论。此外，基于评论对的成对目标可能无法完全捕捉MRHP对整篇评论列表排序的目标，且可能在测试阶段导致泛化能力下降。为解决这些问题，我们提出了一种列表式注意力网络，以清晰捕捉MRHP排序上下文，并设计了一种列表式优化目标以增强模型泛化能力。我们进一步提出使用梯度提升决策树作为评分预测器，以高效划分产品评论的表征。大量实验表明，我们的方法在两个大规模MRHP基准数据集上取得了最先进的结果和优秀的泛化性能。