Unbiased Filtering Of Accidental Clicks in Verizon Media Native Advertising

• URL: http://arxiv.org/abs/2312.05017v1
• Date: Fri, 8 Dec 2023 12:54:30 GMT
• Title: Unbiased Filtering Of Accidental Clicks in Verizon Media Native Advertising
• Authors: Yohay Kaplan and Naama Krasne and Alex Shtoff and Oren Somekh
• Abstract summary: We focus on the challenge of predicting click-through rates (CTR) when we are aware that some of the clicks have short dwell-time. An accidental click implies little affinity between the user and the ad, so predicting that similar users will click on the ad is inaccurate. We present a new approach where the positive weight of the accidental clicks is distributed among all of the negative events (skips), based on their likelihood of causing accidental clicks.
• Score: 1.6717433307723157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Verizon Media (VZM) native advertising is one of VZM largest and fastest growing businesses, reaching a run-rate of several hundred million USDs in the past year. Driving the VZM native models that are used to predict event probabilities, such as click and conversion probabilities, is OFFSET - a feature enhanced collaborative-filtering based event-prediction algorithm. In this work we focus on the challenge of predicting click-through rates (CTR) when we are aware that some of the clicks have short dwell-time and are defined as accidental clicks. An accidental click implies little affinity between the user and the ad, so predicting that similar users will click on the ad is inaccurate. Therefore, it may be beneficial to remove clicks with dwell-time lower than a predefined threshold from the training set. However, we cannot ignore these positive events, as filtering these will cause the model to under predict. Previous approaches have tried to apply filtering and then adding corrective biases to the CTR predictions, but did not yield revenue lifts and therefore were not adopted. In this work, we present a new approach where the positive weight of the accidental clicks is distributed among all of the negative events (skips), based on their likelihood of causing accidental clicks, as predicted by an auxiliary model. These likelihoods are taken as the correct labels of the negative events, shifting our training from using only binary labels and adopting a binary cross-entropy loss function in our training process. After showing offline performance improvements, the modified model was tested online serving VZM native users, and provided 1.18% revenue lift over the production model which is agnostic to accidental clicks.

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