Differentiable Fuzzy Neural Networks for Recommender Systems
This work addresses the need for transparency to increase user trust and compliance in recommender systems, though it appears incremental as it builds on existing neuro-symbolic and fuzzy logic methods.
The paper tackles the problem of transparency in complex recommender systems by proposing differentiable fuzzy neural networks as a neuro-symbolic approach, achieving competitive performance on synthetic and MovieLens 1M datasets while providing transparent decision-making.
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 work-in-progress, we investigate using fuzzy neural networks (FNNs) as a neuro-symbolic approach for recommendations that learn logic-based 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 decision-making process. Finally, the differentiable nature of this approach facilitates an integration with other neural models, enabling the development of hybrid, transparent recommender systems.