Differentiable Fuzzy Neural Networks for Recommender Systems

• URL: http://arxiv.org/abs/2505.06000v1
• Date: Fri, 09 May 2025 12:31:56 GMT
• Title: Differentiable Fuzzy Neural Networks for Recommender Systems
• Authors: Stephan Bartl, Kevin Innerebner, Elisabeth Lex,
• Abstract summary: We investigate using fuzzy neural networks as a neuro-symbolic approach for recommendations.<n>Each rule corresponds to a fuzzy logic expression, making the recommender's decision process inherently transparent.<n>Our results demonstrate that our approach accurately captures user behavior while providing a transparent decision-making process.
• Score: 2.7309692684728613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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