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IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT

Fu, J., Ge, X. , Xin, X., Karatzoglou, A., Arapakis, I., Wang, J. and Jose, J. (2024) IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT. In: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), Washington D.C., USA, 14-18 July 2024, (Accepted for Publication)

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Item Type:Conference Proceedings
Additional Information:Junchen Fu’s research was supported in part by China Scholarship Council (CSC) from the Ministry of Education of China (No. 202308330014).
Keywords:Decoupled parameter-efficient fine-tuning (PEFT), embedded PEFT, full fine-tuning, sequential recommendation, TPME (training-time, parameter, and GPU memory efficiency).
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Ge, Xuri and Wang, Jie and Fu, Junchen
Authors: Fu, J., Ge, X., Xin, X., Karatzoglou, A., Arapakis, I., Wang, J., and Jose, J.
College/School:College of Science and Engineering > School of Computing Science
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