Instance Weighting in Neural Networks for Click-Through Rate Prediction

The instances on which a learning algorithm is most undecided can be weighted more during training to guide the learning model towards spending more effort on the difficult instances. We introduce three instance weighting algorithms to weigh the loss obtained. All of these instance weighting methods...

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Bibliographic Details
Main Author: Bicici, Ergun
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
click-through rate
CTR
instance weighting
Machine learning
Machine learning algorithms
Neural networks
Prediction algorithms
Technological innovation
Training
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Description
Summary:The instances on which a learning algorithm is most undecided can be weighted more during training to guide the learning model towards spending more effort on the difficult instances. We introduce three instance weighting algorithms to weigh the loss obtained. All of these instance weighting methods improve loss, AUC, and F1 results. We demonstrate the improvements on four different classifiers and on two different datasets. The improvements in loss reach 2.8% and in AUC reach 0.73% for Masknet on the Avazu dataset.
ISSN:2770-7946
DOI:10.1109/ASYU58738.2023.10296657