Differentiable Fuzzy Neural Networks for Recommender Systems

TL;DR

This paper proposes a differentiable fuzzy neural network approach for recommender systems that enhances transparency by learning logic-based rules, maintaining competitive performance while providing interpretable decision processes.

Contribution

It introduces a neuro-symbolic method using fuzzy neural networks for transparent recommendations, combining logic-based rules with neural learning.

Findings

Accurately captures user behavior on datasets.

Provides inherently transparent decision explanations.

Maintains competitive recommendation performance.

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.