RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search
In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely on static time-window filtering, resulting in "one-size-fits-all" rankings where content may be chronologically recent but semantically expired. To address the limitation, we present a novel Large Language Models (LLMs)-based Query-Aware Dynamic Content Expiration Prediction Framework deployed in Baidu search, reformulating timeliness as a dynamic validity inference task. Our framework extracts fine-grained temporal contexts from documents and leverages LLMs to deduce a query-specific "validity horizon"-a semant...
| Authors | Tingyu Chen, Wenkai Zhang, Li Gao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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