Antonio Penta

Antonio Penta

Topic Modelling is one of the most prevalent text analysis technique used to explore and retrieve collection of documents. The evaluation of the topic model algorithms is still a very challenging tasks due to the absence of gold-standard list of topics to compare against for every corpus. In this work, we present a specificity score based on keywords properties that is able to select the most informative topics. This approach helps the user to focus on the most informative topics. In the experiments, we show that we are able to compress the state-of-the-art topic modelling results of different factors with an information loss that is much lower than the solution based on the recent coherence score presented in literature.

Ioannis Arapakis, Antonio Penta, Hideo Joho et al.

Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.