Customer shopping behavioral features are core to product search ranking models in eCommerce. In this paper, we investigate the effect of lookback time windows when aggregating these features at the (query, product) level over history. By studying the pros and cons of using long and short time windows, we propose a novel approach to integrating these historical behavioral features of different time windows. In particular, we address the criticality of using query-level vertical signals in ranking models to effectively aggregate all information from different behavioral features. Anecdotal evidence for the proposed approach is also provided using live product search traffic on Walmart.com.

翻译：顾客购物行为特征是电子商务产品搜索排序模型的核心。本文研究了在历史数据中聚合（查询，产品）层面行为特征时，回溯时间窗口的影响。通过分析使用长、短时间窗口的利弊，我们提出了一种整合不同时间窗口历史行为特征的新方法。特别地，我们强调了在排序模型中利用查询层面垂直信号的重要性，以有效聚合来自不同行为特征的所有信息。我们还通过Walmart.com实时产品搜索流量为所提方法提供了实证依据。