ALIGN-Rec: Continual Recommendation under Heterogeneous Unlearning Requests

Recommender systems (RS) continually learn from new user-item interactions while addressing heterogeneous deletion requests. Existing continual recommendation methods focus on retention but do not erase targeted interactions, whereas recommendation unlearning methods are largely offline and do not ensure stability after future learning updates. This creates a critical failure mode: deleted signals can be reactivated through shared user--item representations, which we call collaborative signal regrowth. To address this, we introduce ALIGN-Rec, a model-agnostic online framework for continual recommendation under interleaved learn/unlearn requests. Specifically, ALIGN-Rec tracks low-rank curvature surrogates to identify retain and forget subspaces, constructs compact retain/forget summaries, and performs geometry-aware updates that preserve retained utility while suppressing deleted-signal directions. We instantiate ALIGN-Rec with CARE, an efficient optimizer based on randomized curvature sketching and adaptive summary selection. We provide theoretical guarantees for geometry tracking, summary approximation, and directional suppression in the forget subspace. Extensive experiments across datasets, deletion granularities, and stream settings demonstrate effectiveness aligning with our theoretical guarantees. The code and implementation are available at https://anonymous.4open.science/r/alignrec-FFD8.