Predicting conversions in display advertising based on URL embeddings
This work addresses the need for advertisers to target users likely to convert in real-time bidding, but it is incremental as it applies existing NLP-inspired embedding techniques to a specific domain.
The study tackled the problem of predicting user conversions in display advertising by estimating the probability of conversion based on visited URL histories, and introduced three URL embedding models inspired by NLP to compute semantically meaningful URL representations, with experiments conducted on real logged events from an advertising platform.
Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process. Real-time bidding (RTB) allows the automated trading of ad impressions between advertisers and publishers through real-time auctions. In order to increase the effectiveness of their campaigns, advertisers should deliver ads to the users who are highly likely to be converted (i.e., purchase, registration, website visit, etc.) in the near future. In this study, we introduce and examine different models for estimating the probability of a user converting, given their history of visited URLs. Inspired by natural language processing, we introduce three URL embedding models to compute semantically meaningful URL representations. To demonstrate the effectiveness of the different proposed representation and conversion prediction models, we have conducted experiments on real logged events collected from an advertising platform.