News recommender systems are increasingly driven by black-box models, offering little transparency for editorial decision-making. In this work, we introduce a transparent recommender system that uses fuzzy neural networks to learn human-readable rules from behavioral data for predicting article clicks. By extracting the rules at configurable thresholds, we can control rule complexity and thus, the level of interpretability. We evaluate our approach on two publicly available news datasets (i.e., MIND and EB-NeRD) and show that we can accurately predict click behavior compared to several established baselines, while learning human-readable rules. Furthermore, we show that the learned rules reveal news consumption patterns, enabling editors to align content curation goals with target audience behavior.

翻译：新闻推荐系统日益由黑盒模型驱动，这为编辑决策提供了极少的透明度。本研究提出了一种透明的推荐系统，该系统利用模糊神经网络从行为数据中学习可读规则以预测文章点击。通过在可配置阈值处提取规则，我们可以控制规则复杂度，从而控制可解释性水平。我们在两个公开可用的新闻数据集（即MIND和EB-NeRD）上评估了我们的方法，结果表明，与多个现有基线相比，我们能够准确预测点击行为，同时学习可读规则。此外，我们发现学习的规则揭示了新闻消费模式，使编辑能够将内容策展目标与目标受众行为对齐。