[2604.05379] Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation

Computer Science > Information Retrieval arXiv:2604.05379 (cs) [Submitted on 7 Apr 2026] Title:Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation Authors:Xing Tang, Jingyang Bin, Ziqiang Cui, Xiaokun Zhang, Fuyuan Lyu, Jingyan Jiang, Dugang Liu, Chen Ma, Xiuqiang He View a PDF of the paper titled Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation, by Xing Tang and 8 other authors View PDF HTML (experimental) Abstract:The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often struggle to adapt to real-time preference shifts during inference due to challenges posed by distributional divergence and parameterized constraints. Existing approaches to address this issue include test-time training, test-time augmentation, and retrieval-augmented fine-tuning. However, these methods either introduce significant computational overhead, rely on random augmentation strategies, or require a carefully designed two-stage training paradigm. In this paper, we argue that the key to effective test-time adaptation lies in achieving both effective augmentation and efficient adaptation. To this end, we propose Retrieve-then-Adapt (ReAd), a novel framework that dynamically adapts a deployed SR model to the test distribution through retrieved user preference signals. Specifically, given a trained SR mode...