Introducing Semantic Capability in LinkedIn's Content Search Engine

Fuente: arXiv
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Hauptverfasser: Yang, Xin, Zheng, Rachel, Mohan, Madhumitha, Bhadra, Sonali, Bhatt, Pansul, Lingyu, Zhang, Gupta, Rupesh
Format: Preprint
Veröffentlicht: 2024
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author Yang, Xin
Zheng, Rachel
Mohan, Madhumitha
Bhadra, Sonali
Bhatt, Pansul
Lingyu
Zhang
Gupta, Rupesh
author_facet Yang, Xin
Zheng, Rachel
Mohan, Madhumitha
Bhadra, Sonali
Bhatt, Pansul
Lingyu
Zhang
Gupta, Rupesh
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_20366
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Introducing Semantic Capability in LinkedIn's Content Search Engine
Yang, Xin
Zheng, Rachel
Mohan, Madhumitha
Bhadra, Sonali
Bhatt, Pansul
Lingyu
Zhang
Gupta, Rupesh
Information Retrieval
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.
title Introducing Semantic Capability in LinkedIn's Content Search Engine
topic Information Retrieval
url https://arxiv.org/abs/2412.20366