A general strategy graph collaborative filtering for recommendation unlearning

Recommender systems play a crucial role in delivering personalized services to users, but the increasing volume of user data raises significant concerns about privacy, security, and utility. However, existing machine unlearning methods cannot be directly applied to recommendation systems as they overlook the collaborative information shared across users and items. More recently, a method known as RecEraser was introduced, offering partitioning and aggregation-based approaches. Nevertheless, these approaches have limitations due to their inadequate handling of additional overhead costs. In this paper, we propose A General Strategy Graph Collaborative Filtering for Recommendation Unlearning (GSGCF-RU), which is a novel model-agnostic learnable delete operator that optimizes unlearning edge consistency and feature representation consistency. Specifically, the GSGCF-RU model utilizes unlearning edge consistency to eliminate the influence of deleted elements, followed by feature representation consistency to retain knowledge after deletion. Lastly, experimental results on three real-world public benchmarks demonstrate that GSGCF-RU not only achieves efficient recommendation unlearning but also surpasses state-of-theart methods in terms of model utility. The source code can be found at https://github.com/YongjingHao/GSGCF-RU.