@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://preview.aclanthology.org/fix-sig-urls/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."
}