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.

本文介绍了 MMRecUN，一种用于多模式推荐系统的新框架，通过使用反向和正向 BPR 损失机制来选择性地减弱忘记集合内的相互作用的影响，同时加强保留集合内的相互作用的重要性，从而实现更好的推荐回召率性能，并且相较于基准方法，MMRecUN 在移除目标元素、保留元素性能维持以及开销方面具有更优越的性能。