3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Human Uncertainty and Ranking Error -- The Secret of Successful Evaluation in Predictive Data Mining
Kevin Jasberg, Sergej Sizov
One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or expl…
Re-Evaluating the Netflix Prize - Human Uncertainty and its Impact on Reliability
Kevin Jasberg, Sergej Sizov
In this paper, we examine the statistical soundness of comparative assessments within the field of recommender systems in terms of reliability and human uncertainty. From a control…
Bayesian Brain meets Bayesian Recommender - Towards Systems with Empathy for the Human Nature
Kevin Jasberg, Sergej Sizov
In this paper we consider the modern theory of the Bayesian brain from cognitive neurosciences in the light of recommender systems and expose potentials for our community. In parti…
The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment
Kevin Jasberg, Sergej Sizov
Recommender systems nowadays have many applications and are of great economic benefit. Hence, it is imperative for success-oriented companies to compare different of such systems a…
Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining
Kevin Jasberg, Sergej Sizov
In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem obs…
Assessment of Prediction Techniques: The Impact of Human Uncertainty
Kevin Jasberg, Sergej Sizov
Many data mining approaches aim at modelling and predicting human behaviour. An important quantity of interest is the quality of model-based predictions, e.g. for finding a competi…