Results 41 to 50 of about 2,443,182 (340)
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation [PDF]
arXiv, 2021 Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists.
arxiv
Exposure-Aware Recommendation using Contextual Bandits [PDF]
arXiv, 2022 Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will amplify ...
arxiv
BMC Medical Research Methodology, 2016 BackgroundPatient-reported outcome (PRO) measures play a key role in the advancement of patient-centered care research. The accuracy of inferences, relevance of predictions, and the true nature of the associations made with PRO data depend on the ...
N. Dowling +3 more
semanticscholar +1 more source
Proxy-based Item Representation for Attribute and Context-aware Recommendation [PDF]
, 2023 Neural network approaches in recommender systems have shown remarkable success by representing a large set of items as a learnable vector embedding table. However, infrequent items may suffer from inadequate training opportunities, making it difficult to learn meaningful representations.
arxiv +1 more source
Autism Research, 2016 Diagnostic bias is a concern in autism spectrum conditions (ASC) where prevalence and presentation differ by sex. To ensure that females with ASC are not under‐identified, it is important that ASC screening tools do not systematically underestimate ...
A. Murray +5 more
semanticscholar +1 more source
SA Journal of Human Resource Management, 2017 Orientation: Developing personnel into skilled employees is a major focus of managers and companies. Doing this in a valid way in a cross-culturally diverse working environment may be challenging.
Symen A. Brouwers +2 more
doaj +1 more source
Fairness of Exposure in Dynamic Recommendation [PDF]
arXiv, 2023 Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will ...
arxiv
COVID‐19 and the risk of Alzheimer's disease, amyotrophic lateral sclerosis, and multiple sclerosis
Annals of Clinical and Translational Neurology, Volume 9, Issue 12, Page 1953-1961, December 2022., 2022 Abstract Background The coronavirus disease 2019 (COVID‐19) pandemic has had an unprecedented impact on the healthcare system, economy, and society. Studies have reported that COVID‐19 may cause various neurologic symptoms, including cognitive impairment.
Hanyu Zhang, Zengyuan Zhou
wiley +1 more source
Item parameter estimations for multidimensional graded response model under complex structures
Frontiers in Education, 2022 Item parameter recovery in the compensatory multidimensional graded response model (MGRM) under simple and complex structures with rating-scale item response data was examined.
Olasunkanmi James Kehinde +2 more
doaj +1 more source
Annals of Clinical and Translational Neurology, Volume 9, Issue 12, Page 1962-1973, December 2022., 2022 Abstract Objectives Early‐ and late‐onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain‐specific cognitive function in a well characterized cohort of patients with a biomarker‐based diagnosis.
Adrià Tort‐Merino +16 more
wiley +1 more source