DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors

Zhuoxing Wei; Qingchen Xie; Qi Liu; Jingsong Yu

AAAI 2026technical0 citations

DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors

Zhuoxing Wei, Qingchen Xie, Qi Liu, Jingsong Yu

Abstract

Modeling user interest based on lifelong user behavior sequences is crucial for enhancing Click-Through Rate (CTR) prediction. However, long post-click behavior sequences themselves pose severe performance issues: the sheer volume of data leads to high computational costs and inefficiencies in model training and inference. Traditional methods address this by introducing two-stage approaches, but this compromises model effectiveness due to incomplete utilization of the full sequence context. More importantly, integrating multimodal embeddings into existing large recommendation models (LRM) presents significant challenges: These embeddings often exacerbate computational burdens and mismatch with LRM architectures. To address these issues and enhance the model

BibTeX

@inproceedings{aaai2026_dmginhowmultimod,
  title = {DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors},
  author = {Zhuoxing Wei and Qingchen Xie and Qi Liu and Jingsong Yu},
  booktitle = {AAAI 2026},
  year = {2026}
}