IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
<|reference_start|>IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT: Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory efficiency and training speed. Addressing this gap, our paper introduces IISAN (Intra- and Inter-modal Side Adapted Network for Multimodal Representation), a simple plug-and-play architecture using a Decoupled PEFT structure and exploiting both intra- and inter-modal adaptation.   IISAN matches the performance of full fine-tuning (FFT) and state-of-the-art PEFT. More importantly, it significantly reduces GPU memory usage - from 47GB to just 3GB for multimodal sequential recommendation tasks. Additionally, it accelerates training time per epoch from 443s to 22s compared to FFT. This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.   Furthermore, we propose a new composite efficiency metric, TPME (Training-time, Parameter, and GPU Memory Efficiency) to alleviate the prevalent misconception that "parameter efficiency represents overall efficiency". TPME provides more comprehensive insights into practical efficiency comparisons between different methods. Besides, we give an accessible efficiency analysis of all PEFT and FFT approaches, which demonstrate the superiority of IISAN. We release our codes and other materials at https://github.com/GAIR-Lab/IISAN.<|reference_end|>
@article{fu2024iisan:,
    title={IISAN: Efficiently Adapting Multimodal Representation for Sequential
  Recommendation with Decoupled PEFT},
    author={Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis
  Arapakis, Jie Wang, Joemon M. Jose},
    journal={arXiv preprint arXiv:2404.02059},
    year={2024},
    doi={10.1145/3626772.3657725},
    archivePrefix={arXiv},
    eprint={2404.02059},
    primaryClass={cs.IR cs.CV}
}