Differentiable Fuzzy Neural Networks for Recommender Systems
Stephan Bartl, Kevin Innerebner, Elisabeth Lex · 2025
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 workin-progress, we investigate using fuzzy neural networks (FNNs) as a neuro-symbolic approach for recommendations that learn logicbased 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 decisionmaking process.Finally, the differentiable nature of this approach facilitates an integration with other neural models, enabling the development of hybrid, transparent recommender systems. 1