Introducing Semantic Capability in LinkedIn's Content Search Engine

• URL: http://arxiv.org/abs/2412.20366v2
• Date: Thu, 02 Jan 2025 17:38:21 GMT
• Title: Introducing Semantic Capability in LinkedIn's Content Search Engine
• Authors: Xin Yang, Rachel Zheng, Madhumitha Mohan, Sonali Bhadra, Pansul Bhatt, Lingyu, Zhang, Rupesh Gupta,
• Abstract summary: In the past, most search queries issued to a search engine were short and simple. 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.
• Score: 39.84169929302579
• License:
• 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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