ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations
This addresses the need for transparent and exploration-driven recommendations in sparse or abstract domains, representing an incremental improvement over prior hyperbolic methods.
The paper tackled the problem of creating trustworthy recommender systems by developing ManifoldMind, a probabilistic geometric model that uses adaptive-curvature hyperbolic embeddings for personalized uncertainty modeling and semantic exploration. Experiments on four benchmarks showed superior performance in NDCG, calibration, and diversity compared to strong baselines.
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.