通过匹配得分提升评论推荐准确率
PREMISE: Matching-based Prediction for Accurate Review Recommendation
- 基于多尺度匹配计算评论特征，避免语义重复
- 在两个数据集上性能优于现有方法，且计算成本更低
- 适合需要精准语义匹配的多模态推荐场景
我们提出 PREMISE（PREdict with Matching ScorEs），一种用于多模态评论有用性预测（MRHP）任务的新架构。与以往通过跨模态注意力融合多模态表示的方法不同，PREMISE 先计算多尺度、多领域表示，过滤重复语义，再生成一组匹配得分作为下游推荐任务的特征向量。该架构在上下文匹配内容与目标高度相关的多模态任务中表现显著优于当前最优的融合方法。在两个公开数据集上的实验表明，PREMISE 在保持低计算开销的同时实现了优异性能。
原文摘要 · Abstract (English)
We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Distinct to previous fusion-based methods which obtains multimodal representations via cross-modal attention for downstream tasks, PREMISE computes the multi-scale and multi-field representations, filters duplicated semantics, and then obtained a set of matching scores as feature vectors for the downstream recommendation task. This new architecture significantly boosts the performance for such multimodal tasks whose context matching content are highly correlated to the targets of that task, compared to the state-of-the-art fusion-based methods. Experimental results on two publicly available datasets show that PREMISE achieves promising performance with less computational cost.
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