  Recommendation unlearning is an emerging task to serve users for erasing
unusable data (e.g., some historical behaviors) from a well-trained recommender
model. Existing methods process unlearning requests by fully or partially
retraining the model after removing the unusable data. However, these methods
are impractical due to the high computation cost of full retraining and the
highly possible performance damage of partial training. In this light, a
desired recommendation unlearning method should obtain a similar model as full
retraining in a more efficient manner, i.e., achieving complete, efficient and
harmless unlearning.
  In this work, we propose a new Influence Function-based Recommendation
Unlearning (IFRU) framework, which efficiently updates the model without
retraining by estimating the influence of the unusable data on the model via
the influence function. In the light that recent recommender models use
historical data for both the constructions of the optimization loss and the
computational graph (e.g., neighborhood aggregation), IFRU jointly estimates
the direct influence of unusable data on optimization loss and the spillover
influence on the computational graph to pursue complete unlearning.
Furthermore, we propose an importance-based pruning algorithm to reduce the
cost of the influence function. IFRU is harmless and applicable to mainstream
differentiable models. Extensive experiments demonstrate that IFRU achieves
more than 250 times acceleration compared to retraining-based methods with
recommendation performance comparable to full retraining. Codes are avaiable at
https://github.com/baiyimeng/IFRU.
