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Building a Platform for Ensemble-based Personalized Research Literature Recommendations for AI and Data Science at Zeta Alpha

Published:13 September 2021Publication History

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References

  1. Rocío Cañamares, Marcos Redondo, and Pablo Castells. 2019. Multi-armed recommender system bandit ensembles. https://doi.org/10.1145/3298689.3346984Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Benjamin Charles, Germain Lee, Kyle Lo, Doug Downey, and Daniel S. Weld. 2020. Explanation-Based Tuning of Opaque Machine Learners with Application to Paper Recommendation. https://arxiv.org/abs/2003.0431Google ScholarGoogle Scholar
  3. Kristian Gingstad, Øyvind Jekteberg, and Krisztian Balog. 2020. ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation. https://arxiv.org/abs/2009.11576Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, and Sandor Caetano. 2020. Contextual Meta-Bandit for Recommender Systems Selection. https://doi.org/10.1145/3383313.3412209Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems
    September 2021
    883 pages
    ISBN:9781450384582
    DOI:10.1145/3460231

    Copyright © 2021 Owner/Author

    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: 13 September 2021

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    Qualifiers

    • abstract
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate254of1,295submissions,20%

    Upcoming Conference

    RecSys '24
    18th ACM Conference on Recommender Systems
    October 14 - 18, 2024
    Bari , Italy

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