ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations

TL;DR

ManifoldMind is a probabilistic hyperbolic recommender system that models semantic hierarchies with adaptive curvature spheres, enabling personalized, transparent, and diverse recommendations with improved accuracy and trustworthiness.

Contribution

It introduces a novel adaptive-curvature probabilistic geometric model for recommender systems that enhances semantic exploration and interpretability over prior fixed-geometry methods.

Findings

Outperforms baselines on NDCG, calibration, and diversity metrics

Provides explicit reasoning traces for transparency

Enables trustworthy recommendations in sparse or abstract domains

Abstract

We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed curvature and rigid embeddings, ManifoldMind represents users, items, and tags as adaptive-curvature probabilistic spheres, enabling personalised uncertainty modeling and geometry-aware semantic exploration. A curvature-aware semantic kernel supports soft, multi-hop inference, allowing the model to explore diverse conceptual paths instead of overfitting to shallow or direct interactions. Experiments on four public benchmarks show superior NDCG, calibration, and diversity compared to strong baselines. ManifoldMind produces explicit reasoning traces, enabling transparent, trustworthy, and exploration-driven recommendations in sparse or abstract domains.