In daily fantasy sports (DFS), match participation is highly time-sensitive. Users must act within a narrow window before a game begins, making match recommendation a time-critical task to prevent missed engagement and revenue loss. Existing recommender systems, typically designed for static item catalogs, are ill-equipped to handle the hard temporal deadlines inherent in these live events. To address this, we designed and deployed a recommendation engine using the Deep Interest Network (DIN) architecture. We adapt the DIN architecture by injecting temporality at two levels: first, through real-time urgency features for each candidate match (e.g., time-to-round-lock), and second, via temporal positional encodings that represent the time-gap between each historical interaction and the current recommendation request, allowing the model to dynamically weigh the recency of past actions. This approach, combined with a listwise neuralNDCG loss function, produces highly relevant and urgency-aware rankings. To support this at industrial scale, we developed a multi-node, multi-GPU training architecture on Ray and PyTorch. Our system, validated on a massive industrial dataset with over 650k users and over 100B interactions, achieves a +9% lift in nDCG@1 over a heavily optimized LightGBM baseline with handcrafted features. The strong offline performance of this model establishes its viability as a core component for our planned on-device (edge) recommendation system, where on-line A/B testing will be conducted.

翻译：在每日幻想体育（DFS）中，比赛参与具有高度时间敏感性。用户需在比赛开始前的极短时间窗口内做出决定，这使得比赛推荐成为一项关键任务，旨在防止参与机会流失和收入损失。现有推荐系统通常针对静态物品目录设计，难以应对这类实时赛事固有的硬时间约束。为解决这一问题，我们设计并部署了一种采用深度兴趣网络（DIN）架构的推荐引擎。通过两个层次注入时间因素来适配DIN架构：首先，为每个候选比赛引入实时紧迫感特征（例如距比赛锁定时间）；其次，采用时间位置编码表征每次历史交互与当前推荐请求之间的时间间隔，使模型能动态权衡过往行为的时效性。该方法结合列表式神经NDCG损失函数，可生成高度相关且具备紧迫感感知的排序结果。为支撑工业级规模应用，我们基于Ray和PyTorch开发了多节点多GPU训练架构。该模型在包含65万用户和超过1000亿次交互的工业级数据集上验证，相比经过手工特征优化的LightGBM基准模型，nDCG@1指标提升了9%。其卓越的离线性能确立了模型作为计划中设备端（边缘）推荐系统核心组件的可行性，后续将在该场景中开展在线A/B测试。