Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search

TL;DR

This paper introduces three models that incorporate product demand variations to improve conversion rate prediction in sponsored search, demonstrating enhanced accuracy and robustness in real-world datasets.

Contribution

The paper presents novel models combining demand features, data sampling, and ensemble methods to address demand fluctuations in ad click prediction.

Findings

Models outperform traditional approaches in accuracy.

Models are robust to behavioral fluctuations.

Improved prediction stability in real-world data.

Abstract

In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements. However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior. For example, search engine traffic for florists usually tends to peak around Valentine's day, Mother's day, etc. To overcome, this challenge, in this manuscript we propose three models which are able to incorporate the effects arising due to variations in product demand. The proposed models are a combination of product demand features, specialized data sampling methodologies and ensemble techniques. We demonstrate the performance of our proposed models on datasets obtained from a real-world setting. Our results show that the proposed models more accurately predict the outcome of users interactions with product related advertisements while simultaneously being robust to fluctuations in user and advertiser behaviors.