AdaSRU: Adaptive Source-Free Recommendation Unlearning via Gradient-Constrained Optimization

Recommendation unlearning (RU) aims to remove the influence of specified user--item interactions from a trained recommender while preserving utility on the remaining data. Existing RU methods usually require access to the original training data, especially the remaining data, to recover recommendation utility after unlearning. However, access to such data is often restricted or unavailable in practice due to privacy constraints, storage costs, or third-party deployment, giving rise to source-free recommendation unlearning. To address this challenge, we propose an adaptive source-free recommendation unlearning (AdaSRU) method that enables effective unlearning without accessing the original remaining data. AdaSRU first estimates the inaccessible gradient on the remaining data. It exploits the first-order optimality condition of the trained recommender and approximates the remaining gradient using the forgetting data and squared gradient accumulators from adaptive optimizers, e.g., Adam. AdaSRU then formulates unlearning as a gradient-constrained update, where the estimated remaining gradient is used as a constraint to limit utility degradation while maximizing the unlearning objective. AdaSRU adaptively adjusts the utility-preserving correction according to the current gradient geometry. We further theoretically prove that AdaSRU approaches an approximate Pareto stationary solution. Extensive experiments on three real-world datasets demonstrate that AdaSRU achieves strong performance in both unlearning and recommendation. The code is available at https://anonymous.4open.science/r/AdaSRU-94D9/.