Event-driven Real-time Retrieval in Web Search

• URL: http://arxiv.org/abs/2312.00372v2
• Date: Mon, 4 Dec 2023 11:42:35 GMT
• Title: Event-driven Real-time Retrieval in Web Search
• Authors: Nan Yang, Shusen Zhang, Yannan Zhang, Xiaoling Bai, Hualong Deng, Tianhua Zhou and Jin Ma
• Abstract summary: This paper expands the query with event information that represents real-time search intent. We further enhance the model's capacity for event representation through multi-task training. Our proposed approach significantly outperforms existing state-of-the-art baseline methods.
• Score: 15.235255100530496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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