Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction (2023)

Abstract: 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.

## background in this section, we recall the multimodal review helpfulness prediction (mrhp) problem. then, we introduce theoretical preliminaries which form the basis of our formal analysis of the ranking losses for the mrhp problem in the next section. ## methodology in this section, we elaborate on our proposed architecture, listwise attention network, tree-based helpfulness regressor, and listwise ranking loss along with its comparison against the pairwise one from the theoretical perspective. the overall architecture is illustrated in figure 2 . ## conclusion in this paper, for the mrhp task, we introduce a novel framework to take advantage of the partitioned structure of product review inputs and the ranking nature of the problem. regarding the partitioned preference, we propose a gradientboosted decision tree to route review features towards proper helpfulness subtrees managed by decision nodes. for the ranking nature, we propose listwise attention network and listwise training objective to capture review list-contextualized context. comprehensive analysis provides both theoretical and empirical grounding of our approach in terms of model generalization. experiments on two largescale mrhp datasets showcase the state-of-the-art performance of our proposed framework.