让推荐模型自己学出可读的逻辑规则，解释更透明。
A Logical-Rule Autoencoder for Interpretable Recommendations
- 用可学习的逻辑门自动发现数据中的与/或规则
- 通过权重符号表达否定，不增加维度却支持完整逻辑
- 规则可直接阅读，适合需要透明决策的场景
大多数深度学习推荐模型如同黑箱，依赖难以理解的隐向量进行决策，引发对透明性和责任性的担忧。本文提出一种基于协同过滤的逻辑规则可解释自编码器（LIA），从设计上保证可解释性。LIA在模型中引入可学习的逻辑规则层，每个规则神经元配备门控参数，训练时自动选择与或运算符，从而直接从数据中发现多样化的逻辑模式。为在不增加输入维度的情况下实现逻辑完备性，LIA通过连接权重的正负号编码否定，实现高效表达正负项条件。通过学习显式的、人类可读的重构规则，LIA使用户能直接追踪每条推荐背后的推理过程。大量实验表明，该方法在推荐性能上优于传统基线，同时保持完全可解释性。代码与数据已公开于https://github.com/weibowen555/LIA。
原文摘要 · Abstract (English)
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.
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