Differentiable Fuzzy Neural Networks for Recommender Systems

2025-05-09

AI Summary

Insufficient transparency in recommender systems undermines user trust, algorithmic accountability, and regulatory compliance. Method: We propose the first end-to-end differentiable fuzzy neural network (DFNN) tailored for recommendation tasks. DFNN embeds human-readable fuzzy logic rules directly into a neural architecture and jointly optimizes rule learning and recommendation prediction via gradient-based training. Contribution/Results: By unifying fuzzy logic, differentiable programming, and neuro-symbolic computation, our approach achieves, for the first time in recommendation, both logical interpretability and state-of-the-art (SOTA) predictive performance. Evaluated on MovieLens-1M and synthetic benchmarks, DFNN matches or exceeds SOTA accuracy while generating interpretable rules that are semantically clear, structurally verifiable, and auditable at every decision step. This work establishes a new paradigm for trustworthy recommendation systems grounded in transparent, symbolically grounded reasoning.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Enhancing transparency in complex recommender systems
• Integrating fuzzy neural networks for interpretable rule-based recommendations
• Balancing competitive performance with explainable decision-making processes

Innovation

• Differentiable Fuzzy Neural Networks for transparency
• Neuro-symbolic approach with fuzzy logic rules
• Integration with neural models for hybrid systems