SREraser: Efficient Exact Unlearning for Sequential Recommendation

Sequential recommender systems ingest sensitive personal histories, necessitating machine unlearning to ensure privacy compliance and maintain system utility. Since retraining from scratch is costly and approximate unlearning cannot certify complete data removal, exact unlearning via data sharding has become the prevailing solution. However, most studies in recommendation unlearning have focused on collaborative filtering, and the application of this paradigm to sequential recommendation remains underexplored. Existing methods face two limitations: partitioning strategies scale poorly with the number of users, and aggregators assign a single scalar weight to each sub-model, ignoring how sub-model reliability varies across representation dimensions. In this paper, we propose SREraser, a framework capable of interaction-level unlearning in sequential recommendation. SREraser introduces two core designs. First, User-Centric Spherical Partitioning (UCSP) ranks shards for each user by cosine similarity and resolves shard capacity constraints locally, reducing partitioning cost by an order of magnitude. Second, Adaptive Dimension Aggregation (ADA) produces one weight per representation dimension of every sub-model, conditioned on how closely that sub-model's response aligns with its shard prototype. Experiments on three real-world datasets show that SREraser matches or outperforms other shard-based unlearning methods in recommendation utility, while maintaining comparable unlearning efficiency.