新算法HOPE能同时应对高维上下文的两种稀疏性，提升推荐系统效率。
Navigating Sparsities in High-Dimensional Linear Contextual Bandits
- 提出一种自适应点估计器，可灵活处理参数与协方差矩阵的双重稀疏性
- 在混合稀疏场景下首次实现低后悔率，理论性能优于已有方法
- 适用于推荐、广告等高维决策场景，特别适合数据稀疏的复杂环境
高维线性上下文老虎机问题因维度灾难仍具挑战。现有方法通常只考虑模型参数或上下文协方差矩阵特征值的稀疏性，受限于传统奖励估计器的刚性，适用性有限。本文引入一种强大的逐点估计器，可自适应地同时处理两类稀疏性。基于此，提出新型算法HOPE。理论分析表明，HOPE不仅在已知同质设置（仅一类稀疏）下获得更优后悔界，且首次有效解决两类稀疏混合的异质新场景，展现其灵活性与通用性。实验验证了HOPE在多种场景下均优于现有方法。
原文摘要 · Abstract (English)
High-dimensional linear contextual bandit problems remain a significant challenge due to the curse of dimensionality. Existing methods typically consider either the model parameters to be sparse or the eigenvalues of context covariance matrices to be (approximately) sparse, lacking general applicability due to the rigidity of conventional reward estimators. To overcome this limitation, a powerful pointwise estimator is introduced in this work that adaptively navigates both kinds of sparsity. Based on this pointwise estimator, a novel algorithm, termed HOPE, is proposed. Theoretical analyses demonstrate that HOPE not only achieves improved regret bounds in previously discussed homogeneous settings (i.e., considering only one type of sparsity) but also, for the first time, efficiently handles two new challenging heterogeneous settings (i.e., considering a mixture of two types of sparsity), highlighting its flexibility and generality. Experiments corroborate the superiority of HOPE over existing methods across various scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。