IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
Authors: 
Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. JoseAuthors Info & Claims
Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. JoseAuthors Info & ClaimsPages 687 - 697
Abstract
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 (<u>I</u>ntra- and <u>I</u>nter-modal <u>S</u>ide <u>A</u>dapted <u>N</u>etwork for Multimodal Representation)1 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 code and other materials to facilitate future research. Code is available at https://github.com/GAIR-Lab/IISAN
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Index Terms
- IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
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July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
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This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 11 July 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
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