  Sequential recommender models typically generate predictions in a single step
during testing, without considering additional prediction correction to enhance
performance as humans would. To improve the accuracy of these models, some
researchers have attempted to simulate human analogical reasoning to correct
predictions for testing data by drawing analogies with the prediction errors of
similar training data. However, there are inherent gaps between testing and
training data, which can make this approach unreliable. To address this issue,
we propose an \textit{Abductive Prediction Correction} (APC) framework for
sequential recommendation. Our approach simulates abductive reasoning to
correct predictions. Specifically, we design an abductive reasoning task that
infers the most probable historical interactions from the future interactions
predicted by a recommender, and minimizes the discrepancy between the inferred
and true historical interactions to adjust the predictions.We perform the
abductive inference and adjustment using a reversed sequential model in the
forward and backward propagation manner of neural networks. Our APC framework
is applicable to various differentiable sequential recommender models. We
implement it on three backbone models and demonstrate its effectiveness. We
release the code at https://github.com/zyang1580/APC.
