Dual-task optimization with multi-dimensional feature interaction for influencer recommendation

Abstract

Influencer marketing has emerged as a critical strategy for brands to enhance audience engagement, yet existing recommendation systems often fail to effectively integrate multi-modal features or model complex interactions between brands and influencers. To address these limitations, this paper introduces MFI-IR, a dual-task optimization framework designed to enhance influencer recommendation through multi-dimensional feature interaction. The framework integrates multi-dimensional feature interactions across four key dimensions: cross-modal topic distributions, visual styles, industry labels, and sentiment orientations. By combining explicit polynomial feature interactions with implicit high-order relation mining, MFI-IR dynamically models both shallow and deep feature correlations. A dual-task optimization strategy is designed to jointly minimize matching loss and ranking loss, balancing recommendation accuracy and stability. Experimental results on a publicly available Instagram dataset demonstrate significant performance improvements, achieving an AUC of 0.9371 (6% higher than the best baseline) and a MAP of 0.9079 (3.8$\times$ improvement). The key innovations of this work include: (1) a holistic feature fusion approach that eliminates reliance on single-modality representations by unifying topic, visual, industry, and sentiment features; (2) a hybrid interaction architecture that captures both explicit and implicit feature relationships; and (3) a dual-objective learning mechanism that optimizes matching and ranking tasks simultaneously.