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Activity ranking in LinkedIn feed

Published: 24 August 2014 Publication History

Abstract

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.

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    cover image ACM Conferences
    KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2014
    2028 pages
    ISBN:9781450329569
    DOI:10.1145/2623330
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    Published: 24 August 2014

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    Author Tags

    1. activity ranking
    2. large scale learning
    3. relevance

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    KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;
    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)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)Towards the Evaluation of Recommender Systems with ImpressionsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551483(610-615)Online publication date: 12-Sep-2022
    • (2022)Adversary or Friend? An adversarial Approach to Improving Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546784(369-377)Online publication date: 12-Sep-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
    • (2021)Bias-Variance Decomposition for RankingProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441772(472-480)Online publication date: 8-Mar-2021
    • (2020)ECLIPSEProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525004(704-714)Online publication date: 13-Jul-2020
    • (2020)Deep Learning for Search and Recommender Systems in PracticeProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3406709(3515-3516)Online publication date: 23-Aug-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
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