用模糊神经网络让新闻推荐可解释，规则可读且准确。
Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks
- 用模糊神经网络从行为数据中提取可读规则
- 在MIND和EB-NeRD数据集上预测点击率接近基线
- 规则揭示读者行为模式，适合编辑优化内容策略
新闻推荐系统日益依赖黑箱模型，缺乏对编辑决策的透明性。本文提出一种透明推荐系统，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。通过设置可配置的阈值提取规则，可控制规则复杂度，从而调节可解释性。我们在两个公开新闻数据集（MIND 和 EB-NeRD）上评估该方法，结果表明其预测点击行为的准确性优于多个基准模型，同时能生成人类可读规则。此外，学习到的规则揭示了新闻消费模式，使编辑能够将内容策划目标与目标受众行为对齐。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。