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Workshop on Online and Adaptative Recommender Systems (OARS)

Published: 14 August 2021 Publication History

Abstract

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. In addition, there have been many advances that make online and adaptive recommender systems (OARS) feasible, scalable, and more sophisticated. This workshop aims to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.

Cited By

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  • (2024)Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825459(2334-2339)Online publication date: 15-Dec-2024
  • (2021)Introduction to the Special Section on Graph Technologies for User Modeling and Recommendation, Part 2ACM Transactions on Information Systems10.1145/349018040:3(1-5)Online publication date: 14-Dec-2021
  • (2021)Graph Technologies for User Modeling and Recommendation: Introduction to the Special Issue - Part 1ACM Transactions on Information Systems10.1145/347759640:2(1-5)Online publication date: 27-Sep-2021

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  1. Workshop on Online and Adaptative Recommender Systems (OARS)

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    cover image ACM Conferences
    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
    August 2021
    4259 pages
    ISBN:9781450383325
    DOI:10.1145/3447548
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 August 2021

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    Author Tags

    1. adaptive
    2. online
    3. real-time
    4. recommender systems

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    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    KDD '25

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    Cited By

    View all
    • (2024)Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825459(2334-2339)Online publication date: 15-Dec-2024
    • (2021)Introduction to the Special Section on Graph Technologies for User Modeling and Recommendation, Part 2ACM Transactions on Information Systems10.1145/349018040:3(1-5)Online publication date: 14-Dec-2021
    • (2021)Graph Technologies for User Modeling and Recommendation: Introduction to the Special Issue - Part 1ACM Transactions on Information Systems10.1145/347759640:2(1-5)Online publication date: 27-Sep-2021

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