Unlearning methods for recommender systems (RS) have emerged to address privacy issues and concerns about legal compliance. However, evolving user preferences and content licensing issues still remain unaddressed. This is particularly true in case of multi-modal recommender systems (MMRS), which aim to accommodate the growing influence of multi-modal information on user preferences. Previous unlearning methods for RS are inapplicable to MMRS due to incompatibility of multi-modal user-item behavior data graph with the matrix based representation of RS. Partitioning based methods degrade recommendation performance and incur significant overhead costs during aggregation. This paper introduces MMRecUN, a new framework for multi-modal recommendation unlearning, which, to the best of our knowledge, is the first attempt in this direction. Given the trained recommendation model and marked forget data, we devise Reverse Bayesian Personalized Ranking (BPR) objective to force the model to forget it. MMRecUN employs both reverse and forward BPR loss mechanisms to selectively attenuate the impact of interactions within the forget set while concurrently reinforcing the significance of interactions within the retain set. Our experiments demonstrate that MMRecUN outperforms baseline methods across various unlearning requests when evaluated on benchmark multi-modal recommender datasets. MMRecUN achieves recall performance improvements of up to $\mathbf{49.85%}$ compared to the baseline methods. It is up to $\mathbf{1.3}\times$ faster than the \textsc{Gold} model, which is trained on retain data from scratch. MMRecUN offers advantages such as superior performance in removing target elements, preservation of performance for retained elements, and zero overhead costs in comparison to previous methods.

翻译：推荐系统（RS）的遗忘方法已出现，旨在解决隐私问题和法律合规性担忧。然而，不断演变的用户偏好和内容许可问题仍未得到解决。对于多模态推荐系统（MMRS）而言尤其如此，这类系统旨在适应多模态信息对用户偏好日益增长的影响。由于多模态用户-物品行为数据图与基于矩阵的RS表示不兼容，先前针对RS的遗忘方法不适用于MMRS。基于分区的方法会降低推荐性能，并在聚合过程中产生显著的开销成本。本文提出了MMRecUN，一种用于多模态推荐遗忘的新框架，据我们所知，这是该方向上的首次尝试。给定训练好的推荐模型和标记的遗忘数据，我们设计了反向贝叶斯个性化排序（BPR）目标，以强制模型遗忘该数据。MMRecUN同时采用反向和正向BPR损失机制，选择性地减弱遗忘集内交互的影响，同时强化保留集内交互的重要性。我们的实验表明，在基准多模态推荐数据集上评估时，MMRecUN在各种遗忘请求下均优于基线方法。与基线方法相比，MMRecUN实现了高达$\mathbf{49.85\%}$的召回性能提升。相比从头在保留数据上训练的\textsc{Gold}模型，其速度提升高达$\mathbf{1.3}$倍。与先前方法相比，MMRecUN具有移除目标元素性能更优、保留元素性能得以保持以及零开销成本等优势。