@inproceedings{han-etal-2025-premise,
title = "{PREMISE}: Matching-based Prediction for Accurate Review Recommendation",
author = "Han, Wei and
Chen, Hui and
Poria, Soujanya",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.150/",
doi = "10.18653/v1/2025.findings-naacl.150",
pages = "2776--2794",
ISBN = "979-8-89176-195-7",
abstract = "We present PREMISE, a new architecture for the matching-based learning in the multimodal fields for the 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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<abstract>We present PREMISE, a new architecture for the matching-based learning in the multimodal fields for the 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.</abstract>
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%0 Conference Proceedings
%T PREMISE: Matching-based Prediction for Accurate Review Recommendation
%A Han, Wei
%A Chen, Hui
%A Poria, Soujanya
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F han-etal-2025-premise
%X We present PREMISE, a new architecture for the matching-based learning in the multimodal fields for the 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.
%R 10.18653/v1/2025.findings-naacl.150
%U https://aclanthology.org/2025.findings-naacl.150/
%U https://doi.org/10.18653/v1/2025.findings-naacl.150
%P 2776-2794
Markdown (Informal)
[PREMISE: Matching-based Prediction for Accurate Review Recommendation](https://aclanthology.org/2025.findings-naacl.150/) (Han et al., Findings 2025)
ACL