Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
This addresses the challenge of fluctuating demand for advertisers in online advertising, but it is incremental as it builds on existing methods with specific adaptations.
The paper tackled the problem of conversion rate prediction in sponsored search being impacted by variations in product demand, such as seasonal peaks, and proposed models that incorporate demand features, sampling, and ensembles to improve accuracy and robustness. The results show more accurate predictions and robustness to fluctuations in user and advertiser behaviors.
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.