用可微模糊神经网络实现透明推荐，决策过程像逻辑规则一样可读。
Differentiable Fuzzy Neural Networks for Recommender Systems
- 将模糊逻辑规则嵌入神经网络，用可微方式学习用户偏好
- 在电影推荐数据集上性能接近顶尖模型，且推理过程清晰可解释
- 适合需要透明度的推荐场景，如金融、医疗等高风险领域
随着推荐系统日益复杂，透明性对提升用户信任、责任归属和合规性至关重要。神经符号方法通过融合符号推理与子符号学习，为构建透明、以用户为中心的系统提供了前景。本文研究将模糊神经网络（FNNs）作为推荐的神经符号方法，基于预定义的人类可读原子学习基于逻辑的规则。每条规则对应一个模糊逻辑表达式，使推荐决策过程天然透明。与黑箱机器学习方法相比，该方法在保持竞争力性能的同时揭示了推荐背后的推理过程。我们在合成数据集和MovieLens 1M数据集上评估该方法，并与当前最先进的推荐算法进行对比。结果表明，该方法能准确捕捉用户行为，同时提供透明的决策流程。最后，该方法的可微特性使其易于与其他神经模型集成，支持构建混合式透明推荐系统。
原文摘要 · Abstract (English)
As recommender systems become increasingly complex, transparency is essential to increase user trust, accountability, and regulatory compliance. Neuro-symbolic approaches that integrate symbolic reasoning with sub-symbolic learning offer a promising approach toward transparent and user-centric systems. In this work-in-progress, we investigate using fuzzy neural networks (FNNs) as a neuro-symbolic approach for recommendations that learn logic-based rules over predefined, human-readable atoms. Each rule corresponds to a fuzzy logic expression, making the recommender's decision process inherently transparent. In contrast to black-box machine learning methods, our approach reveals the reasoning behind a recommendation while maintaining competitive performance. We evaluate our method on a synthetic and MovieLens 1M datasets and compare it to state-of-the-art recommendation algorithms. Our results demonstrate that our approach accurately captures user behavior while providing a transparent decision-making process. Finally, the differentiable nature of this approach facilitates an integration with other neural models, enabling the development of hybrid, transparent recommender systems.
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