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Personalized Federated Search at LinkedIn

Authors:

Dhruv Arya,

Viet Ha-Thuc,

Shakti SinhaAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1699 - 1702

https://doi.org/10.1145/2806416.2806615

Published: 17 October 2015 Publication History

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Abstract

LinkedIn has grown to become a platform hosting diverse sources of information ranging from member profiles, jobs, professional groups, slideshows etc. Given the existence of multiple sources, when a member issues a query like "software engineer", the member could look for software engineer profiles, jobs or professional groups. To tackle this problem, we exploit a data-driven approach that extracts searcher intents from their profile data and recent activities at a large scale. The intents such as job seeking, hiring, content consuming are used to construct features to personalize federated search experience. We tested the approach on the LinkedIn homepage and A/B tests show significant improvements in member engagement. As of writing this paper, the approach powers all of federated search on LinkedIn homepage.

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Malik ANguyen TBudhwar P(2024)Towards a Conceptual Model of AI-Mediated Knowledge Sharing Exchange of HRM Practices: Antecedents and ConsequencesIEEE Transactions on Engineering Management10.1109/TEM.2022.316311771(13083-13095)Online publication date: 2024
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Gupta SKakde VDharamshi RSakarkar GAbiyasa A(2024)Employment Prediction System Based on Recommender System Using NLP, Content Filtering & Collaborative Filtering2024 10th International Conference on Smart Computing and Communication (ICSCC)10.1109/ICSCC62041.2024.10690402(95-98)Online publication date: 25-Jul-2024
https://doi.org/10.1109/ICSCC62041.2024.10690402
Ostuni VKofler CNilange MLamkhede SZylberglejd D(2023)Search Personalization at NetflixCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587675(756-758)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587675
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Index Terms

Personalized Federated Search at LinkedIn
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Published In

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2015

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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https://doi.org/10.1109/TEM.2022.3163117
Gupta SKakde VDharamshi RSakarkar GAbiyasa A(2024)Employment Prediction System Based on Recommender System Using NLP, Content Filtering & Collaborative Filtering2024 10th International Conference on Smart Computing and Communication (ICSCC)10.1109/ICSCC62041.2024.10690402(95-98)Online publication date: 25-Jul-2024
https://doi.org/10.1109/ICSCC62041.2024.10690402
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https://dl.acm.org/doi/10.1145/3543873.3587675
Garba AWu SKhalid S(2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
https://doi.org/10.1007/s10115-023-01922-6
Haris MStocker MAuer S(2022)Enriching Scholarly Knowledge with ContextWeb Engineering10.1007/978-3-031-09917-5_10(148-161)Online publication date: 5-Jul-2022
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Haris MFarfar KStocker MAuer S(2021)Federating Scholarly Infrastructures with GraphQLTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_24(308-324)Online publication date: 1-Dec-2021
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Padhya MJinwala D(2019)MULKASE: a novel approach for key-aggregate searchable encryption for multi-owner dataFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180019220:12(1717-1748)Online publication date: 29-Oct-2019
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Sardar VRam SH. SSridharan S(2019)Resource Selection in Federated Web Search2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/CONFLUENCE.2019.8776610(654-657)Online publication date: Jan-2019
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Brovman YJacob MSrinivasan NNeola SGalron DSnyder RWang PSen SGeyer WFreyne JCastells P(2016)Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize ConversionProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959166(199-202)Online publication date: 7-Sep-2016
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