Support the Creation of Effective Ad Creatives
Table 2. Comparison of the prediction performance of CVs in mean squared error (MSE) criteria. The proposed multi-task learning and conditional attention reduced MSE in almost all the categories, especially estimating cases where the number of conversions (#CV) is one or more (#CV>0). However, “All predicted as zero” showed sufficiently low MSE in this category, due to too many CV=0 in this dataset. Therefore, we conclude using MSE as an evaluation metric is not suitable in this study.
['Model', 'Model', 'MSE All', 'MSE All', 'MSE #CV >0', 'MSE #CV >0']
Almost all the results show that the model applying the multi-task learning and conditional attention mechanism had a smaller MSE score than the other models did. Overall, the RNN-based GRU showed better performance than the baseline models. Therefore, the results suggest that it is important to properly capture word order when evaluating creative texts. Compared with vanilla and attention, in the proposed model, conditional attention showed a better performance.