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Introducing Semantic Capability in LinkedIn's Content Search Engine
December 29, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Xin Yang, Rachel Zheng, Madhumitha Mohan, Sonali Bhadra, Pansul Bhatt, Lingyu, Zhang, Rupesh Gupta
arXiv ID
2412.20366
Category
cs.IR: Information Retrieval
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In the past, most search queries issued to a search engine were short and simple. A keyword based search engine was able to answer such queries quite well. However, members are now developing the habit of issuing long and complex natural language queries. Answering such queries requires evolution of a search engine to have semantic capability. In this paper we present the design of LinkedIn's new content search engine with semantic capability, and its impact on metrics.
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