  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.
