Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require transparency and accountability. In this work, we propose a Logical-rule Interpretable Autoencoder (LIA) for collaborative filtering that is interpretable by design. LIA introduces a learnable logical rule layer in which each rule neuron is equipped with a gate parameter that automatically selects between AND and OR operators during training, enabling the model to discover diverse logical patterns directly from data. To support functional completeness without doubling the input dimensionality, LIA encodes negation through the sign of connection weights, providing a parameter-efficient mechanism for expressing both positive and negated item conditions within each rule. By learning explicit, human-readable reconstruction rules, LIA allows users to directly trace the decision process behind each recommendation. Extensive experiments show that our method achieves improved recommendation performance over traditional baselines while remaining fully interpretable. Code and data are available at https://github.com/weibowen555/LIA.

翻译：大多数深度学习推荐模型作为黑盒运行，依赖模糊其决策过程的隐式表示。这种内在可解释性的缺失引发了需要透明性和可问责性的应用中的担忧。在这项工作中，我们提出了一种用于协同过滤的逻辑规则可解释自编码器（LIA），该模型在设计上具有可解释性。LIA引入了一个可学习的逻辑规则层，其中每个规则神经元配备了一个门控参数，在训练过程中自动选择AND或OR运算符，使模型能够直接从数据中发现多样的逻辑模式。为在不使输入维度翻倍的情况下支持功能完备性，LIA通过连接权重的符号编码否定，为在每个规则中表达正负项目条件提供了一种参数高效的机制。通过学习显式、可读的重构规则，LIA允许用户直接追溯每次推荐背后的决策过程。大量实验表明，我们的方法在保持完全可解释性的同时，实现了优于传统基线的推荐性能。代码和数据可在 https://github.com/weibowen555/LIA 获取。