HyperML：一种推荐系统的双曲空间提升度量学习方法
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
摘要：
本文研究了在非欧空间中学习用户和项目表示的概念。具体来说，我们通过探索Mobius gyrovector空间来研究双曲空间中的度量学习与协同过滤之间的联系，其中空间的形式主义可以用来推广最常见的欧几里得向量运算。总体而言，这项工作的目的是通过度量学习方法在推荐系统中弥合欧氏几何和双曲几何之间的差距。我们提出了HyperML（双曲度量学习），这是一个概念简单但非常有效的模型，可以提高性能。通过一系列广泛的实验，我们表明，我们提出的HyperML不仅优于他们的欧几里德同行，而且还实现了最先进的性能在多个基准数据集上，证明了个性化推荐的有效性在双曲几何。
英文摘要：
This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.
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