Information retrieval in real-time search presents unique challenges distinct
from those encountered in classical web search. These challenges are
particularly pronounced due to the rapid change of user search intent, which is
influenced by the occurrence and evolution of breaking news events, such as
earthquakes, elections, and wars. Previous dense retrieval methods, which
primarily focused on static semantic representation, lack the capacity to
capture immediate search intent, leading to inferior performance in retrieving
the most recent event-related documents in time-sensitive scenarios. To address
this issue, this paper expands the query with event information that represents
real-time search intent. The Event information is then integrated with the
query through a cross-attention mechanism, resulting in a time-context query
representation. We further enhance the model's capacity for event
representation through multi-task training. Since publicly available datasets
such as MS-MARCO do not contain any event information on the query side and
have few time-sensitive queries, we design an automatic data collection and
annotation pipeline to address this issue, which includes ModelZoo-based Coarse
Annotation and LLM-driven Fine Annotation processes. In addition, we share the
training tricks such as two-stage training and hard negative sampling. Finally,
we conduct a set of offline experiments on a million-scale production dataset
to evaluate our approach and deploy an A/B testing in a real online system to
verify the performance. Extensive experimental results demonstrate that our
proposed approach significantly outperforms existing state-of-the-art baseline
methods.

本文研究信息检索在实时搜索中面临的挑战，通过整合事件信息和查询，利用跨注意力机制实现时间背景的查询表示，通过多任务训练增强事件表示能力，以及通过两阶段训练和硬负采样进行模型优化，并通过实验证明本方法在时间敏感场景下显著优于现有基线方法。