# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 5 Experimental Results and Discussion
### Results Discussion  
The analysis of findings from the extracted sample data for users and images confirms that personalized annotation recommendations on CH platforms can improve the user's experience and efforts for finding images that matched their preferences, rather than a browsing process searching for appealing images, which could be completely unsatisfactory. In general, the three classifiers (random forest, neural networks, and support vector machines) perform with comparable accuracy in the classification task, with a result rounding to 60% of average precision, revealing a fairly balanced classification model. In view of our results, classifying images among users resulted in relatively high average accuracy rates, with the NN classifier achieving the best accuracy of 68.2% and the RF achieving the lowest accuracy of 66.4%. In our opinion, incorrectly predicted images are highly influenced by the nature and availability of the dataset, particularly the history of previous annotations performed by the system users, which affects the training of the classifier prediction model. However, the word embedding method alleviates this obvious limitation and aids in the conducting of experiments and testing.  
Generally, for classification models, the tuning of parameters such as the number of estimators in an RF classifier and the number of hidden layer nodes in a NN classifier has a significant impact on the classification accuracy. Furthermore, after extensive testing, we conclude that, although choosing an activation function and solvers for a neural network is important, other factors, such as the learning parameters and the number of hidden layer nodes, are more crucial for effective network training as the accuracy results show only minor differences when training with different activation functions. On the other hand, the selection of the activation function in SVM had an obvious effect on the training process and, as a result, the classification accuracy with an average of 10% difference in accuracy was recorded. To be more specific, the _linear_ kernel in the SVM was sufficient to train the features and find a clear separable hyperplane for this dataset, whereas non-linear kernels (_rbf_, _polynomial_, _sigmoid_) required longer training times to converge despite some  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline _Kernel Function_ & \begin{tabular}{c} **Training-** \\ **Testing** \\ \end{tabular} & **Accuracy \%** & \begin{tabular}{c} **Average** \\ **Precision** \\ \end{tabular} & \begin{tabular}{c} **F-Score** \\ **(Macro)** \\ \end{tabular} & \
\begin{tabular}{c} **F-Score** \\ **(Micro)** \\ \end{tabular} &
\begin{tabular}{c} **F-Score** \\ **(Weighted)** \\ \end{tabular} \\ \hline \multirow{3}{*}{_Linear_} & 60–40 & 65.3 & 0.59 & 0.620 & 0.653 & 0.620 \\  & 70–30 & 65.4 & 0.591 & 0.620 & 0.654 & 0.620 \\  & 80–20 & 67 & 0.61 & 0.640 & 0.670 & 0.640 \\  & 60–40 & 55.8 & 0.53 & 0.510 & 0.558 & 0.510 \\ \multirow{3}{*}{_Sigmoid_} & 70–30 & 56 & 0.53 & 0.560 & 0.560 & 0.560 \\  & 80–20 & 56.4 & 0.54 & 0.540 & 0.564 & 0.541 \\  & 60–40 & 65.5 & 0.591 & 0.620 & 0.655 & 0.621 \\ _rbf_ & 70–30 & 65.7 & 0.591 & 0.621 & 0.657 & 0.621 \\  & 80–20 & 67.2 & 0.62 & 0.641 & 0.672 & 0.641 \\  & 60–40 & 62.2 & 0.57 & 0.590 & 0.622 & 0.588 \\ \multirow{3}{*}{_Polynomial_} & 70–30 & 62.3 & 0.57 & 0.590 & 0.623 & 0.589 \\  & 80–20 & 63.9 & 0.58 & 0.612 & 0.640 & 0.612 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tests results for support vector machine classifier.  
having similar accuracy rates. According to the results obtained, the _rbf_ kernel performs similar to the _linear_ kernel, making it the second choice recommended when the problem is non-linearly separable. Furthermore, although the sigmoid function was quite prominent for the SVM due to its origin from NN, it produces lower classification accuracy when used with the SVM than when used to train the data for the NN classifier on this dataset.