Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation

TL;DR

This paper introduces MICRec, a unified framework that combines inductive, multimodal, and cross-domain learning to improve the robustness and generalizability of recommendation systems across diverse and incomplete data scenarios.

Contribution

The paper proposes MICRec, a novel unified model that integrates multiple learning paradigms to enhance recommendation performance in complex, real-world settings.

Findings

MICRec outperforms 12 baseline models in various domains.

Significant improvements in data-sparse environments.

Effective use of overlapping users as domain anchors.

Abstract

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.