12 citations · 12 across the 15 of their papers we have counts for
4 papers · 2 filters
Are Multimodal Embeddings Truly Beneficial for Recommendation? A Deep Dive into Whole vs. Individual Modalities
Yu Ye, Junchen Fu, Yu Song +2
Multimodal recommendation has emerged as a mainstream paradigm, typically leveraging text and visual embeddings extracted from pre-trained models such as Sentence-BERT, Vision Tran…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
Hongji Li, Hanwen Du, Youhua Li +5
The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities such as text, images, videos, and audio for mor…
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Junchen Fu, Yongxin Ni, Joemon M. Jose +4
In this paper, we explore a less-studied yet practically important problem: how to efficiently and effectively adapt multiple (2) multimodal foundation models (MFMs) for the seq…