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.

翻译：序列推荐模型通常在测试阶段单步生成预测，而不像人类那样通过额外的预测修正来提升性能。为提高这些模型的精度，部分研究者尝试模拟人类的类比推理，通过借鉴相似训练数据的预测误差来修正测试数据的预测。然而，测试数据与训练数据之间存在固有差异，可能导致该方法不可靠。为此，我们提出一种面向序列推荐的\textit{溯因预测修正}（APC）框架。该方法模拟溯因推理来修正预测：具体而言，我们设计了一个溯因推理任务，从推荐器预测的未来交互中推断最可能的历史交互，并通过最小化推断结果与真实历史交互之间的差异来调整预测。我们利用逆向序列模型，以神经网络的前向传播和反向传播方式执行溯因推理与调整。所提出的APC框架适用于各类可微分的序列推荐模型，我们在三种基础模型上进行了实现并验证了其有效性。代码已开源在https://github.com/zyang1580/APC。