Recommendation Unlearning via Matrix Correction
Jiahao Liu Dongsheng Li Hansu Gu Tun Lu Jiongran Wu Peng Zhang Li Shang Ning Gu
Recommender systems are important for providing personalized services to
users, but the vast amount of collected user data has raised concerns about
privacy (e.g., sensitive data), security (e.g., malicious data) and utility
(e.g., toxic data). To address these challenges, recommendation unlearning has
emerged as a promising approach, which allows specific data and models to be
forgotten, mitigating the risks of sensitive/malicious/toxic user data.
However, existing methods often struggle to balance completeness, utility, and
efficiency, i.e., compromising one for the other, leading to suboptimal
recommendation unlearning. In this paper, we propose an Interaction and Mapping
Matrices Correction (IMCorrect) method for recommendation unlearning. Firstly,
we reveal that many collaborative filtering (CF) algorithms can be formulated
as mapping-based approach, in which the recommendation results can be obtained
by multiplying the user-item interaction matrix with a mapping matrix. Then,
IMCorrect can achieve efficient recommendation unlearning by correcting the
interaction matrix and enhance the completeness and utility by correcting the
mapping matrix, all without costly model retraining. Unlike existing methods,
IMCorrect is a whitebox model that offers greater flexibility in handling
various recommendation unlearning scenarios. Additionally, it has the unique
capability of incrementally learning from new data, which further enhances its
practicality. We conducted comprehensive experiments to validate the
effectiveness of IMCorrect and the results demonstrate that IMCorrect is
superior in completeness, utility, and efficiency, and is applicable in many
recommendation unlearning scenarios.