Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks
This provides a transparent method for news editors to align content curation with audience behavior, though it is incremental as it builds on existing fuzzy neural network techniques.
The authors tackled the lack of transparency in news recommender systems by developing a fuzzy neural network that learns human-readable rules from behavioral data to predict article clicks, achieving accurate predictions on MIND and EB-NeRD datasets compared to baselines.
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.