Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations

• URL: http://arxiv.org/abs/2508.11978v1
• Date: Sat, 16 Aug 2025 08:34:17 GMT
• Title: Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations
• Authors: Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov,
• Abstract summary: We introduce a novel hyperbolic recommendation model that uses geometrical insights to improve representation learning.<n>Our approach not only outperforms existing Euclidean and hyperbolic models but also reduces popularity bias, leading to more diverse and personalized recommendations.
• Score: 0.6827423171182154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have demonstrated the potential of hyperbolic geometry for capturing complex patterns from interaction data in recommender systems. In this work, we introduce a novel hyperbolic recommendation model that uses geometrical insights to improve representation learning and increase computational stability at the same time. We reformulate the notion of hyperbolic distances to unlock additional representation capacity over conventional Euclidean space and learn more expressive user and item representations. To better capture user-items interactions, we construct a triplet loss that models ternary relations between users and their corresponding preferred and nonpreferred choices through a mix of pairwise interaction terms driven by the geometry of data. Our hyperbolic approach not only outperforms existing Euclidean and hyperbolic models but also reduces popularity bias, leading to more diverse and personalized recommendations.

Related papers

• On the Geometric Accuracy of Implicit and Primitive-based Representations Derived from View Rendering Constraints [15.120134539151236]
  We compare implicit and explicit Novel View Synthesis methods for space-based 3D object reconstruction.<n>We show embeddings do not translate into meaningful gains in geometric accuracy.<n>Our findings clarify the limits of appearance embeddings for geometry-centric tasks.
  arXiv  Detail & Related papers  (2025-09-12T13:37:18Z)
• Knowledge Graph Completion with Mixed Geometry Tensor Factorization [0.6827423171182154]
  We propose a new geometric approach for knowledge graph completion via low rank tensor approximation.<n>We augment a pretrained and well-established Euclidean model based on a Tucker tensor decomposition with a novel hyperbolic interaction term.
  arXiv  Detail & Related papers  (2025-04-03T13:54:43Z)
• Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU [50.9588132578029]
  This paper investigates machine unlearning in hyperbolic contrastive learning.<n>We adapt Alignment to MERU, a model that embeds images and text in hyperbolic space to better capture semantic hierarchies.<n>Our approach introduces hyperbolic-specific components including entailment calibration and norm regularization that leverage the unique properties of hyperbolic space.
  arXiv  Detail & Related papers  (2025-03-19T12:47:37Z)
• Robust Hyperbolic Learning with Curvature-Aware Optimization [7.89323764547292]
  Current hyperbolic learning approaches are prone to overfitting, computationally expensive, and prone to instability.<n>We introduce a novel fine-tunable hyperbolic scaling approach to constrain hyperbolic embeddings reduce approximation errors.<n>Our approach demonstrates consistent improvements across Computer Vision, EEG classification, and hierarchical metric learning tasks.
  arXiv  Detail & Related papers  (2024-05-22T20:30:14Z)
• Hyperbolic Delaunay Geometric Alignment [52.835250875177756]
  We propose a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets.
  arXiv  Detail & Related papers  (2024-04-12T17:14:58Z)
• Hyperbolic vs Euclidean Embeddings in Few-Shot Learning: Two Sides of the Same Coin [49.12496652756007]
  We show that the best few-shot results are attained for hyperbolic embeddings at a common hyperbolic radius. In contrast to prior benchmark results, we demonstrate that better performance can be achieved by a fixed-radius encoder equipped with the Euclidean metric.
  arXiv  Detail & Related papers  (2023-09-18T14:51:46Z)
• Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction [76.5549647815413]
  We propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet) Our method learns mesh features with rich geometry-image multi-modal information and models better hand-object interaction.
  arXiv  Detail & Related papers  (2023-09-06T13:00:10Z)
• HICF: Hyperbolic Informative Collaborative Filtering [35.26872278129825]
  hyperbolic space is well-suited to describe the power-law distributed user-item network. It is unclear which kinds of items can be effectively recommended by the hyperbolic model and which cannot. We propose a novel learning method, named hyperbolic informative collaborative filtering (HICF), to compensate for the recommendation effectiveness of the head item.
  arXiv  Detail & Related papers  (2022-07-19T03:45:38Z)
• HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization [52.369435664689995]
  We introduce a textitHyperbolic Regularization powered Collaborative Filtering (HRCF) and design a geometric-aware hyperbolic regularizer. Specifically, the proposal boosts optimization procedure via the root alignment and origin-aware penalty. Our proposal is able to tackle the over-smoothing problem caused by hyperbolic aggregation and also brings the models a better discriminative ability.
  arXiv  Detail & Related papers  (2022-04-18T06:11:44Z)
• Hyperbolic Vision Transformers: Combining Improvements in Metric Learning [116.13290702262248]
  We propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. We evaluate the proposed model with six different formulations on four datasets.
  arXiv  Detail & Related papers  (2022-03-21T09:48:23Z)
• Hyperbolic Manifold Regression [33.40757136529844]
  We consider the problem of performing manifold-valued regression onto an hyperbolic space as an intermediate component for a number of relevant machine learning applications. We propose a novel perspective on two challenging tasks: 1) hierarchical classification via label embeddings and 2) taxonomy extension of hyperbolic representations. Our experiments show that the strategy of leveraging the hyperbolic geometry is promising.
  arXiv  Detail & Related papers  (2020-05-28T10:16:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.