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.

翻译：推荐系统在为用户提供个性化服务方面具有重要意义，但收集的海量用户数据引发了隐私（如敏感数据）、安全（如恶意数据）和效用（如毒性数据）等方面的担忧。为应对这些挑战，推荐遗忘作为一种具有前景的方法应运而生，它允许遗忘特定数据和模型，从而降低敏感/恶意/毒性用户数据带来的风险。然而，现有方法往往难以兼顾完整性、效用性和效率，即牺牲某一指标以换取其他指标，导致推荐遗忘效果欠佳。本文提出一种面向推荐遗忘的交互与映射矩阵修正（IMCorrect）方法。首先，我们揭示了许多协同过滤（CF）算法可以表述为基于映射的方法，其中推荐结果可通过用户-物品交互矩阵与映射矩阵相乘获得。接着，IMCorrect通过修正交互矩阵实现高效的推荐遗忘，并通过修正映射矩阵增强完整性与效用性，整个过程无需昂贵的模型重新训练。与现有方法不同，IMCorrect是一种白盒模型，在处理各种推荐遗忘场景时具有更高的灵活性。此外，它具备从新数据中增量学习的独特能力，进一步提升了其实用性。我们通过全面实验验证了IMCorrect的有效性，结果表明IMCorrect在完整性、效用性和效率方面均具有优越性，并适用于多种推荐遗忘场景。