3 Matching Annotations
  1. Nov 2020
  2. psyarxiv.com psyarxiv.com
    Review of Best Practice Recommendations for Ensuring High Quality Data with Amazon’s Mechanical Turk
    1
    1. marta_radosevic 18 Nov 2020

      Bauer, B., Larsen, K. L., Caulfield, N., Elder, D., Jordan, S., & Capron, D. (2020). Review of Best Practice Recommendations for Ensuring High Quality Data with Amazon’s Mechanical Turk. PsyArXiv. https://doi.org/10.31234/osf.io/m78sf

      is:preprint lang:en data interpretation accuracy low-quality data crowdsourcing Amazon's Mechanical Turk MTurk recommendation online data methodology
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    psyarxiv.com/m78sf/
  3. Sep 2020
  4. psyarxiv.com psyarxiv.com
    Unifying recommendation and active learning for information filtering and recommender systems
    1
    1. katietaylor_99 07 Sep 2020

      Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.

      is:preprint lang:en active learning information filtering recommender system algorithms Internet AI artificial intelligence machine learning predictive accuracy recommendation accuracy exploration-exploitation tradeoff parameterized model cognitive science computer science experimental approach
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    psyarxiv.com/jqa83/
  5. Aug 2020
  6. www.nature.com www.nature.com
    Extracting user influence from ratings and trust for rating prediction in recommendations
    1
    1. marta_radosevic 14 Aug 2020

      Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1

      is:article lang:en recommendation trust prediction rating influence collaborative filtering recommendation systems algorithm model accuracy internet social rating
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    nature.com/articles/s41598-020-70350-1