User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS). They recommend diverse content such as products, social media posts, TikTok reels, etc., based on a user-item interaction graph. With rising data privacy demands, recent methods propose unlearning private user data from uni-modal recommender systems (RS). However, methods for unlearning item data related to outdated user preferences, revoked licenses, and legally requested removals are still largely unexplored. Previous RS unlearning methods are unsuitable for MMRS due to the incompatibility of their matrix-based representation with the multi-modal user-item interaction graph. Moreover, their data partitioning step degrades performance on each shard due to poor data heterogeneity and requires costly performance aggregation across shards. This paper introduces MMRecUn, the first approach known to us for unlearning in MMRS and unlearning item data. Given a trained RS model, MMRecUn employs a novel Reverse Bayesian Personalized Ranking (BPR) objective to enable the model to forget marked data. The reverse BPR attenuates the impact of user-item interactions within the forget set, while the forward BPR reinforces the significance of user-item interactions within the retain set. Our experiments demonstrate that MMRecUn outperforms baseline methods across various unlearning requests when evaluated on benchmark MMRS datasets. MMRecUn achieves recall performance improvements of up to 49.85% compared to baseline methods and is up to $\mathbf{1.3}\times$ faster than the Gold model, which is trained on retain set from scratch. MMRecUn offers significant advantages, including superiority in removing target interactions, preserving retained interactions, and zero overhead costs compared to previous methods. The code will be released after review.

翻译：用户数据跨多种模态的分布推动了多模态推荐系统（MMRS）的普及。此类系统基于用户-物品交互图，为用户推荐多样化的内容，如商品、社交媒体帖子、TikTok短视频等。随着数据隐私需求的日益增长，近期研究提出了从单模态推荐系统（RS）中遗忘私有用户数据的方法。然而，针对与过时用户偏好、已撤销许可及法律要求删除相关的物品数据的遗忘方法仍鲜有探索。以往的RS遗忘方法因其基于矩阵的表征与多模态用户-物品交互图不兼容，不适用于MMRS。此外，其数据划分步骤因数据异构性不足导致各分片性能下降，且需承担跨分片性能聚合的高昂成本。本文提出MMRecUn，这是我们所知首个面向MMRS的遗忘方法，并首次处理物品数据遗忘问题。给定一个已训练的RS模型，MMRecUn采用一种新颖的逆向贝叶斯个性化排序（BPR）目标，使模型能够遗忘标记数据。逆向BPR衰减遗忘集中用户-物品交互的影响，而正向BPR则增强保留集中用户-物品交互的重要性。我们在基准MMRS数据集上的实验表明，针对各类遗忘请求，MMRecUn均优于基线方法。与基线方法相比，MMRecUn的召回性能提升最高达49.85%，且比在保留集上从头训练的Gold模型快至$\mathbf{1.3}\times$。相较于先前方法，MMRecUn在移除目标交互、保留有效交互以及零额外开销方面具有显著优势。代码将在评审后公开。