Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items. Unlike universal e-commerce platforms such as Taobao and Amazon, user behaviors and interests on the OFD platform are more location and time-sensitive due to limited delivery ranges and regional commodity supplies. However, existing CTR prediction algorithms in OFD scenarios concentrate on capturing interest from historical behavior sequences, which fails to effectively model the complex spatiotemporal information within features, leading to poor performance. To address this challenge, this paper introduces the Contrastive Sres under different search states using three modules: contrastive spatiotemporal representation learning (CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal information filter (StIF). CSRL utilizes a contrastive learning framework to generate a spatiotemporal activation representation (SAR) for the search action. StPE employs SAR to activate users' diverse preferences related to location and time from the historical behavior sequence field, using a multi-head attention mechanism. StIF incorporates SAR into a gating network to automatically capture important features with latent spatiotemporal effects. Extensive experiments conducted on two large-scale industrial datasets demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a significant 0.88% lift in CTR, which has substantial business implications.

翻译：点击率预测是在线即时配送（OFD）平台精准估计用户点击食品项目概率的关键任务。与淘宝、亚马逊等通用电商平台不同，受限于有限的配送范围和区域商品供给，OFD平台上的用户行为和兴趣更具位置与时间敏感性。然而，现有面向OFD场景的点击率预测算法主要聚焦于从历史行为序列中捕获兴趣，未能有效建模特征中的复杂时空信息，导致性能不佳。针对这一挑战，本文提出了一种对比时空偏好模型（CSPM），通过在搜索动作中利用对比性时空表示学习（CSRL）、时空偏好提取器（StPE）和时空信息过滤器（StIF）三个模块来隐式建模不同搜索状态下的用户意图。CSRL采用对比学习框架为搜索动作生成时空激活表示（SAR）。StPE利用多头注意力机制，通过SAR从历史行为序列字段中激活用户与位置和时间相关的多样化偏好。StIF将SAR融入门控网络，自动捕获具有潜在时空影响的重要特征。在两个大规模工业数据集上进行的广泛实验表明，CSPM达到了最先进的性能。值得注意的是，CSPM已成功部署于阿里巴巴的在线OFD平台饿了么，使点击率显著提升0.88%，具有重要的商业应用价值。