稀疏优化在电商精准营销中的应用

The Application of Sparse Optimization in Precision Marketing of E-Commerce

摘要: 电商精准营销的推进往往被高维稀疏用户数据以及异常值干扰所限制，本文将非光滑损失、部分稀疏和部分组稀疏的优化思路融合，利用光滑化交替临近梯度算法展开求解，达成关键特征筛选、群组结构改善和抗异常值的协同成果，在没有异常值的情形下，算法得到的解同真实最优解十分接近，能够在稀疏降维的同时保证求解精度；而当异常值被注入时，解依然稳定聚集在真实值周围，具备较强的抗干扰能力，经过实验验证，此方法在电商精准触达用户的过程中，能够明显提升营销转化率，同时优化资源投放逻辑以避免错配浪费、提升效率，为电商领域用户分层运营、精准选品等精细运作场景，提供切实的技术支撑。

Abstract: To address the challenges of handling high-dimensional sparse user data and resisting outlier interference in e-commerce precision marketing, this paper proposes an optimization method integrating non-smooth loss, partial sparsity, and partial group sparsity, and designs the smoothing alternating gradient algorithm, for solving. In the scenario without outliers, the solutions output by the algorithm closely fit the true optimal values, ensuring the solution accuracy while achieving sparse dimensionality reduction of features and groups. When outliers are injected, the solutions still gather stably around the baseline, demonstrating strong anti-interference ability. Experiments and practical applications on e-commerce platforms verify that this method can significantly enhance the marketing conversion rate in the process of precisely reaching users in e-commerce. At the same time, it optimizes the logic of resource allocation to avoid misallocation and waste, and improves efficiency. It provides practical technical support for the fine operation scenarios such as user stratification operation and precise product selection in the e-commerce field.