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Semantic Search At LinkedIn

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Borisyuk, Fedor, Vasudevan, Sriram, Wu, Muchen, Li, Guoyao, Le, Benjamin, Zhang, Shaobo, Shen, Qianqi Kay, Juan, Yuchin, Behdin, Kayhan, Dong, Liming, Yang, Kaixu, Jing, Shusen, Pothamsetty, Ravi, Arora, Rajat, Sheng, Sophie Yanying, Abdrashitov, Vitaly, Zhao, Yang, Su, Lin, Wang, Xiaoqing, Zheng, Chujie, Metkar, Sarang, Gupta, Rupesh, Lapchuk, Igor, Racca, David N., Mohan, Madhumitha, Li, Yanbo, Li, Haojun, Gandhi, Saloni, Lu, Xueying, Bhole, Chetan, Hooshmand, Ali, Yang, Xin, Muthuregunathan, Raghavan, Zhang, Jiajun, Teoh, Mathew, Coler, Adam, Gupta, Abhinav, Ma, Xiaojing, Ramachandran, Sundara Raman, Ramezani, Morteza, Wang, Yubo, Zhang, Lijuan, Li, Richard, Sheng, Jian, Nguyen, Chanh, Chen, Yen-Chi, Zhu, Chuanrui, Zhang, Claire, Xu, Jiahao, Kulkarni, Deepti, Lan, Qing, Subramaniam, Arvind, Fatahibaarzi, Ata, Shimizu, Steven, Chen, Yanning, Wang, Zhipeng, He, Ran, Zhou, Zhengze, Song, Qingquan, Dai, Yun, Johnson, Caleb, Liu, Ping, Gharghabi, Shaghayegh, Mohanasundaram, Gokulraj, Bottaro, Juan, Sachindran, Santhosh, Guo, Qi, Ren, Yunxiang, Jiang, Chengming, Mo, Di, Simon, Luke, Shen, Jianqiang, Wu, Jingwei, Zhang, Wenjing
Format:	Preprint
Publié:	2026
Sujets:	Information Retrieval Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
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_version_	1866918326639263744
author	Borisyuk, Fedor Vasudevan, Sriram Wu, Muchen Li, Guoyao Le, Benjamin Zhang, Shaobo Shen, Qianqi Kay Juan, Yuchin Behdin, Kayhan Dong, Liming Yang, Kaixu Jing, Shusen Pothamsetty, Ravi Arora, Rajat Sheng, Sophie Yanying Abdrashitov, Vitaly Zhao, Yang Su, Lin Wang, Xiaoqing Zheng, Chujie Metkar, Sarang Gupta, Rupesh Lapchuk, Igor Racca, David N. Mohan, Madhumitha Li, Yanbo Li, Haojun Gandhi, Saloni Lu, Xueying Bhole, Chetan Hooshmand, Ali Yang, Xin Muthuregunathan, Raghavan Zhang, Jiajun Teoh, Mathew Coler, Adam Gupta, Abhinav Ma, Xiaojing Ramachandran, Sundara Raman Ramezani, Morteza Wang, Yubo Zhang, Lijuan Li, Richard Sheng, Jian Nguyen, Chanh Chen, Yen-Chi Zhu, Chuanrui Zhang, Claire Xu, Jiahao Kulkarni, Deepti Lan, Qing Subramaniam, Arvind Fatahibaarzi, Ata Shimizu, Steven Chen, Yanning Wang, Zhipeng He, Ran Zhou, Zhengze Song, Qingquan Dai, Yun Johnson, Caleb Liu, Ping Gharghabi, Shaghayegh Mohanasundaram, Gokulraj Bottaro, Juan Sachindran, Santhosh Guo, Qi Ren, Yunxiang Jiang, Chengming Mo, Di Simon, Luke Shen, Jianqiang Wu, Jingwei Zhang, Wenjing
author_facet	Borisyuk, Fedor Vasudevan, Sriram Wu, Muchen Li, Guoyao Le, Benjamin Zhang, Shaobo Shen, Qianqi Kay Juan, Yuchin Behdin, Kayhan Dong, Liming Yang, Kaixu Jing, Shusen Pothamsetty, Ravi Arora, Rajat Sheng, Sophie Yanying Abdrashitov, Vitaly Zhao, Yang Su, Lin Wang, Xiaoqing Zheng, Chujie Metkar, Sarang Gupta, Rupesh Lapchuk, Igor Racca, David N. Mohan, Madhumitha Li, Yanbo Li, Haojun Gandhi, Saloni Lu, Xueying Bhole, Chetan Hooshmand, Ali Yang, Xin Muthuregunathan, Raghavan Zhang, Jiajun Teoh, Mathew Coler, Adam Gupta, Abhinav Ma, Xiaojing Ramachandran, Sundara Raman Ramezani, Morteza Wang, Yubo Zhang, Lijuan Li, Richard Sheng, Jian Nguyen, Chanh Chen, Yen-Chi Zhu, Chuanrui Zhang, Claire Xu, Jiahao Kulkarni, Deepti Lan, Qing Subramaniam, Arvind Fatahibaarzi, Ata Shimizu, Steven Chen, Yanning Wang, Zhipeng He, Ran Zhou, Zhengze Song, Qingquan Dai, Yun Johnson, Caleb Liu, Ping Gharghabi, Shaghayegh Mohanasundaram, Gokulraj Bottaro, Juan Sachindran, Santhosh Guo, Qi Ren, Yunxiang Jiang, Chengming Mo, Di Simon, Luke Shen, Jianqiang Wu, Jingwei Zhang, Wenjing
contents	Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search, combining an LLM relevance judge, embedding-based retrieval, and a compact Small Language Model trained via multi-teacher distillation to jointly optimize relevance and engagement. A prefill-oriented inference architecture co-designed with model pruning, context compression, and text-embedding hybrid interactions boosts ranking throughput by over 75x under a fixed latency constraint while preserving near-teacher-level NDCG, enabling one of the first production LLM-based ranking systems with efficiency comparable to traditional approaches and delivering significant gains in quality and user engagement.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_07309
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Semantic Search At LinkedIn Borisyuk, Fedor Vasudevan, Sriram Wu, Muchen Li, Guoyao Le, Benjamin Zhang, Shaobo Shen, Qianqi Kay Juan, Yuchin Behdin, Kayhan Dong, Liming Yang, Kaixu Jing, Shusen Pothamsetty, Ravi Arora, Rajat Sheng, Sophie Yanying Abdrashitov, Vitaly Zhao, Yang Su, Lin Wang, Xiaoqing Zheng, Chujie Metkar, Sarang Gupta, Rupesh Lapchuk, Igor Racca, David N. Mohan, Madhumitha Li, Yanbo Li, Haojun Gandhi, Saloni Lu, Xueying Bhole, Chetan Hooshmand, Ali Yang, Xin Muthuregunathan, Raghavan Zhang, Jiajun Teoh, Mathew Coler, Adam Gupta, Abhinav Ma, Xiaojing Ramachandran, Sundara Raman Ramezani, Morteza Wang, Yubo Zhang, Lijuan Li, Richard Sheng, Jian Nguyen, Chanh Chen, Yen-Chi Zhu, Chuanrui Zhang, Claire Xu, Jiahao Kulkarni, Deepti Lan, Qing Subramaniam, Arvind Fatahibaarzi, Ata Shimizu, Steven Chen, Yanning Wang, Zhipeng He, Ran Zhou, Zhengze Song, Qingquan Dai, Yun Johnson, Caleb Liu, Ping Gharghabi, Shaghayegh Mohanasundaram, Gokulraj Bottaro, Juan Sachindran, Santhosh Guo, Qi Ren, Yunxiang Jiang, Chengming Mo, Di Simon, Luke Shen, Jianqiang Wu, Jingwei Zhang, Wenjing Information Retrieval Artificial Intelligence Machine Learning Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search, combining an LLM relevance judge, embedding-based retrieval, and a compact Small Language Model trained via multi-teacher distillation to jointly optimize relevance and engagement. A prefill-oriented inference architecture co-designed with model pruning, context compression, and text-embedding hybrid interactions boosts ranking throughput by over 75x under a fixed latency constraint while preserving near-teacher-level NDCG, enabling one of the first production LLM-based ranking systems with efficiency comparable to traditional approaches and delivering significant gains in quality and user engagement.
title	Semantic Search At LinkedIn
topic	Information Retrieval Artificial Intelligence Machine Learning
url	https://arxiv.org/abs/2602.07309