统一归纳、跨域与多模态学习，提升推荐系统泛化能力
Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation
- 融合归纳建模、多模态信息与跨域迁移，统一处理复杂推荐场景
- 在数据稀疏领域上优于12个基线模型，尤其在低资源场景表现突出
- 适合需要跨域泛化与多源信息整合的推荐系统研究者
推荐系统传统上依赖用户与物品的交互建模，近年研究试图拓展至新用户/物品的泛化、多源信息融合及跨域知识迁移。然而，这些工作多聚焦单一方向，难以应对真实世界中复杂的跨域推荐需求。本文提出MICRec，一个统一框架，融合归纳建模、多模态引导与跨域迁移，以捕捉异构且不完整的现实数据中的用户上下文与潜在偏好。相比INMO的归纳主干，本模型通过基于模态的聚合增强表达能力，并利用跨域重叠用户作为锚点缓解数据稀疏问题，实现鲁棒且可泛化的推荐。实验表明，MICRec超越12个基线模型，在训练数据有限的领域中取得显著提升。
原文摘要 · Abstract (English)
Recommender systems have long been built upon the modeling of interactions between users and items, while recent studies have sought to broaden this paradigm by generalizing to new users and items, incorporating diverse information sources, and transferring knowledge across domains. Nevertheless, these efforts have largely focused on individual aspects, hindering their ability to tackle the complex recommendation scenarios that arise in daily consumptions across diverse domains. In this paper, we present MICRec, a unified framework that fuses inductive modeling, multimodal guidance, and cross-domain transfer to capture user contexts and latent preferences in heterogeneous and incomplete real-world data. Moving beyond the inductive backbone of INMO, our model refines expressive representations through modality-based aggregation and alleviates data sparsity by leveraging overlapping users as anchors across domains, thereby enabling robust and generalizable recommendation. Experiments show that MICRec outperforms 12 baselines, with notable gains in domains with limited training data.
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