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

• URL: http://arxiv.org/abs/2604.05379v1
• Date: Tue, 07 Apr 2026 03:24:56 GMT
• Title: Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
• Abstract summary: sequential recommendation (SR) models often struggle to adapt to real-time preference shifts during inference.<n>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.<n>Extensive experiments across five benchmark datasets demonstrate that ReAd consistently outperforms existing SR methods.
• Score: 17.159956938670174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 model, ReAd first retrieves collaboratively similar items for a test user from a constructed collaborative memory database. A lightweight retrieval learning module then integrates these items into an informative augmentation embedding that captures both collaborative signals and prediction-refinement cues. Finally, the initial SR prediction is refined via a fusion mechanism that incorporates this embedding. Extensive experiments across five benchmark datasets demonstrate that ReAd consistently outperforms existing SR methods.

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