@@ -79,8 +79,12 @@ The following experiment shows the impact of missing user feedback to the traini

In this section I compare the performance of the personalized models between iteration steps. Therefore the base model is applied to one of the training data sets of a participant, which is refined by one of the filter configurations. After that the resulted personalized model is evaluated. This step is repeated over all training sets where the previous base model is replaced by the new model. Additionally I evaluate the performance of a single iteration step by always training and evaluating the base model on the respective training data. I repeat that experiment with different amounts of training epochs and for the two regularization approaches of \secref{sec:approachRegularization}.

\subsubsection{Evolution}

First we observe how the model performance evolves over the iteration steps. \figref{arg1} shows the S scores for each iteration step of the overall personalized model and single trained model. The training data is generated by the \texttt{all\_noise\_hwgt} filter configuration. The epochs and regularization are the same as of the previous experiments. We can see, that the first iteration leads

First we observe how the model performance evolves over the iteration steps. \figref{arg1} shows the S scores for each iteration step of the overall personalized model and single trained model. The training data is generated by the \texttt{all\_noise\_hwgt} filter configuration. In graph (a) epochs and regularization are the same as of the previous experiments. We can see, that the first iteration leads a lower S score than the general model. But for all following iteration steps, the performance increases continuously. Although the single step model has a lower S score in the second iteration, the iterated model still benefits from the training. Similarly, in graph (b) the overall personalized models performance increases with each iteration step despite of oscillating values of the single models. This illustrates that personalization does not depend on the last training step, but accumulates data across all iterations.

\subsubsection{Comparison of filter configurations}

In this step I compare the evaluation of the personalized model over the different filter configurations.

\section{Evaluation of personalization}

\subsection{Compare Active learning with my approach}