  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.
