PREMISE: Matching-based Prediction for Accurate Review Recommendation
This work addresses the problem of accurate review recommendation in multimodal contexts, but it appears incremental as it builds on existing matching-based approaches with specific optimizations.
The authors tackled the multimodal review helpfulness (MRHP) task by introducing PREMISE, a matching-based architecture that computes multi-scale and multi-field representations and uses matching scores as features, achieving promising performance with less computational cost compared to state-of-the-art fusion-based methods on two datasets.
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.