用双曲几何优化推荐系统，提升表达能力与多样性
Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations
- 基于双曲空间重构距离度量，增强用户/物品表征能力
- 设计混合配对项的三元组损失，更好建模用户偏好关系
- 相比传统模型更少热门偏差，适合追求多样推荐场景
近期研究证实双曲几何在捕捉推荐系统交互数据复杂模式方面的潜力。本文提出一种新颖的双曲推荐模型，利用几何洞察同时提升表示学习能力与计算稳定性。通过重构双曲距离定义，释放超越传统欧氏空间的表示容量，学习更具表现力的用户与物品表征。为更好捕捉用户-物品交互，构建一种三元组损失，通过由数据几何驱动的混合成对项，建模用户与其偏好与非偏好选择之间的三元关系。该双曲方法不仅优于现有欧氏及双曲模型，还有效降低流行度偏差，带来更丰富且个性化的推荐结果。
原文摘要 · Abstract (English)
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.
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