Predicting clicks in online display advertising with latent features and side-information

With the growing popularity of the Internet as a media, new technologies for targeting advertisements in the digital domain, a discipline generally referred to as computational advertising, have opened up to new business models for publishers and advertisers to finance their services and sell their products. Online advertising entails using banner ads as a means to attract user attention towards a certain brand or product. The clicks, known as click-throughs, take a user to a website specified by the advertiser and generates revenue for the page displaying the banner, which we call the publisher. In real-time bidding (RTB) banner ads are determined and placed in real-time based on an auction initiated by the publisher between all potential advertisers, asking them to place a bid of what they are willing to pay for the current impression (displaying the ad), given information about the page, the user engaging the page, a description of the banner format and placement on the page. The advertiser with the highest bid wins the auction and their banner is displayed to the user. RTB thus requires advertisers, or more commonly, the demand side platforms (DSPs) acting on behalf of the advertisers, to be able to estimate the potential value of an impression, given the available information. A key measure for evaluating the potential values of impressions is the click-through rate (CTR), calculated as the ratio of the number of clicks over the total number of impressions in a specific context. What we are investigating in the present work, is a model for predicting CTRs, even in the face of contexts without any 2 previous clicks and/or very few impressions available, such that the empirical CTR can be unknown or very poorly estimated.