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Personalizing LinkedIn Feed

Published: 10 August 2015 Publication History

Abstract

LinkedIn dynamically delivers update activities from a user's interpersonal network to more than 300 million members in the personalized feed that ranks activities according their "relevance" to the user. This paper discloses the implementation details behind this personalized feed system at LinkedIn which can not be found from related work, and addresses the scalability and data sparsity challenges for deploying the system online. More specifically, we focus on the personalization models by generating three kinds of affinity scores: Viewer-ActivityType Affinity, Viewer-Actor Affinity, and Viewer-Actor-ActivityType Affinity. Extensive experiments based on online bucket tests (A/B experiments) and offline evaluation illustrate the effect of our personalization models in LinkedIn feed.

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  • (2023)Disentangling and Operationalizing AI Fairness at LinkedInProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594075(1213-1228)Online publication date: 12-Jun-2023
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    cover image ACM Conferences
    KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2015
    2378 pages
    ISBN:9781450336642
    DOI:10.1145/2783258
    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 ACM 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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    Publication History

    Published: 10 August 2015

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    Author Tags

    1. feed relevance
    2. large scale learning
    3. personalization

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    KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    Cited By

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    • (2023)Disentangling and Operationalizing AI Fairness at LinkedInProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594075(1213-1228)Online publication date: 12-Jun-2023
    • (2023)Modelling Delayed Redemption with Importance Sampling and Pre-Redemption EngagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599867(3926-3936)Online publication date: 6-Aug-2023
    • (2023)Evaluating Bert and GPT-2 Models for Personalised Linkedin Post Recommendation2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307957(1-7)Online publication date: 6-Jul-2023
    • (2022)CS-RAD: Conditional Member Status Refinement and Ability Discovery for Social Network ApplicationsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539046(3486-3494)Online publication date: 14-Aug-2022
    • (2022)Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction ModelsArtificial Intelligence and Its Applications10.1007/978-3-030-96311-8_19(197-209)Online publication date: 12-Mar-2022
    • (2020)ECLIPSEProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525004(704-714)Online publication date: 13-Jul-2020
    • (2020)A Counterfactual Framework for Seller-Side A/B Testing on MarketplacesProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401434(2288-2296)Online publication date: 25-Jul-2020
    • (2020)Ads Allocation in Feed via Constrained OptimizationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403391(3386-3394)Online publication date: 23-Aug-2020
    • (2020)Edge formation in Social Networks to Nurture Content CreatorsProceedings of The Web Conference 202010.1145/3366423.3380267(1999-2008)Online publication date: 20-Apr-2020
    • (2019)Log2IntentProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330889(1055-1063)Online publication date: 25-Jul-2019
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