Multimodal Neural Framework with Hybrid Loss for Recommendation, Finance, and Healthcare Applications

Abstract

This paper presents a Multimodal Neural Framework with Hybrid Loss designed to leverage multiform signals across disparate application sectors — from personalized recommender systems to financial fraud detection and healthcare diagnostics. The main novelty lies in the framework’s ability to learn unified representations from heterogeneous data sources through specialized encoders and a hybrid-loss objective. The multiform signals — including tabular attributes, text reviews, imaging, and transactions — collectively enable the framework to outperform methods that rely on a single view of the data. Our extensive experiments across well-established benchmark datasets, and an exhaustive ablation study, underscore the utility of multiform signals and hybrid-loss in improving both robustness and accuracy.