VAMIRec: Value-Aware Memory Intervention for Continual Recommendation

Continual recommendation captures dynamic user interests amid sequential interactions, enabling models to adapt to new observations while preserving prior valuable knowledge. Existing continual recommendation methods primarily preserve or reuse historical knowledge through replay, distillation, or regularization to mitigate forgetting during incremental updates. However, noisy or obsolete inherited representations render indiscriminate preservation detrimental by inducing memory conflicts with current stage preferences. Moreover, rapid shifts in user interests require timely and selective memory updates, while coarse global mechanisms fail to provide such fine-grained responsiveness. To address these issues, we propose VAMIRec, a value-aware memory intervention framework for continual recommendation. VAMIRec first constructs a memory state by separating inherited user memory into a slow anchor component and a fast-changing drift component. We further design a value-aware action evaluation module to generate three candidate operations, and estimate conservative action values by accounting for uncertainty and intervention cost. Finally, we develop a policy-guided memory intervention module that distills the training-time action supervision into an inference-safe policy and applies the selected memory action before all-item ranking. Extensive experiments on three real-world datasets demonstrate that VAMIRec consistently outperforms the state-of-the-art baselines.