Kevin Jasberg

According to our database1, Kevin Jasberg authored at least 14 papers between 2017 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2021
Modelling Human Uncertainty in Predictive Data Mining: Development of a Neuro-Stochastic Model to Describe Unreliable User Feedback and its Impact on User-Adaptive Information Systems.
PhD thesis, 2021

2020
Human uncertainty in explicit user feedback and its impact on the comparative evaluations of accurate prediction and personalisation.
Behav. Inf. Technol., 2020

2019
Computational Approaches to Access Probabilistic Population Codes for Higher Cognition an Decision-Making.
CoRR, 2019

2018
Unsicherheiten menschlicher Entscheidungsfindung in Empfehlungssystemen.
Inf. Wiss. Prax., 2018

Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Recommendation and Personalisation.
CoRR, 2018

Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting.
CoRR, 2018

Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation.
Proceedings of the Web Information Systems Engineering - WISE 2018, 2018

Human uncertainty and ranking error: fallacies in metric-based evaluation of recommender systems.
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018

2017
Human Uncertainty and Ranking Error - The Secret of Successful Evaluation in Predictive Data Mining.
CoRR, 2017

Re-Evaluating the Netflix Prize - Human Uncertainty and its Impact on Reliability.
CoRR, 2017

Bayesian Brain meets Bayesian Recommender - Towards Systems with Empathy for the Human Nature.
CoRR, 2017

Assessment of Prediction Techniques: The Impact of Human Uncertainty.
Proceedings of the Web Information Systems Engineering - WISE 2017, 2017

Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining.
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017

The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment.
Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017


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