Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks

TL;DR

This paper presents a transparent news recommendation system using fuzzy neural networks that learns human-readable rules from behavioral data, balancing accuracy and interpretability to aid editorial decisions.

Contribution

It introduces a novel fuzzy neural network approach for transparent news recommendations, enabling rule extraction at adjustable complexity levels.

Findings

Accurately predicts article clicks on MIND and EB-NeRD datasets.

Learns human-readable rules that reveal news consumption patterns.

Supports editorial decision-making with interpretable insights.

Abstract

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.