AI 中文总结
研究工业广告推荐中稀疏特征排序问题，提出仅用CPU的LO-FAR工作流程，通过轻量级局部估计器排序，在生产数据集上约两小时完成排序，保留下游归一化熵增益，证明简单局部过滤器在成本和周转约束下是实用生产选择。
AI 中文摘要
工业广告推荐模型严重依赖于编码用户历史和上下文标识符的稀疏、高基数ID列表特征。这些特征由专用嵌入表支持，在存储、训练和服务成本中占主导地位。稀疏特征排序不仅是离线建模问题，还受计算预算和迭代节奏限制。我们提出了局部特征排序（LO-FAR），一种仅使用CPU、与模型无关的工作流程，使用轻量级局部估计器对每个候选特征进行排序。在一个包含超过一百万条日志交互和475个稀疏ID列表特征的生产数据集上，LO-FAR在大约两个CPU小时内完成排序，并在CTR和CVR任务中保留了与基于洗牌的重要性、二元随机神经元以及基于覆盖的启发式方法相当的下游归一化熵增益。贡献在于展示了一个可部署的工作流程，表明在成本和周转约束严格时，简单的局部过滤器是比更复杂的交互感知方法更实用的生产选择。
英文摘要
Industrial ad recommendation models rely heavily on sparse, high-cardinality ID-list features that encode user histories and contextual identifiers. Each is backed by a dedicated embedding table, so these features dominate storage, training, and serving cost and must be revisited as traffic and downstream models evolve. Therefore, sparse feature ranking is not just an offline modeling problem but also a recurrent systems decision limited by compute budgets and iteration cadence. We present Localized Feature Ranking (LO-FAR), a CPU-only, model-agnostic workflow that ranks each candidate feature from its stand-alone held-out predictive signal using lightweight local estimators rather than the GPU-bound retraining loops of permutation- and stochastic-gate-based methods. On a production dataset of more than one million logged interactions and 475 sparse ID-list features, LO-FAR completes ranking in approximately two CPU-hours and preserves downstream Normalized Entropy gains on CTR and CVR tasks that are competitive with shuffle-based importance, Binary Stochastic Neurons, and a coverage-based heuristic across budgets of 100--400 retained features. The contribution is a deployable workflow showing that, when cost and turnaround constraints are binding, a simple local filter can be a practical production choice over heavier interaction-aware alternatives.
Journal refRecSys 2026
