2007 | OriginalPaper | Buchkapitel
Prediction of Keyword Auction Using Bayesian Network
verfasst von : Liwen Hou, Liping Wang, Kang Li
Erschienen in: E-Commerce and Web Technologies
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Online keyword auctions, in which marketers bid for advertising slots along the search engine results, have become a new channel of advertisement. To better manage the advertisement campaign, a key challenge for advertisers is to predict each keyword’s bidding price and effectiveness (e.g. click through rate), which are not priorly known to the individual advertiser. This paper identifies those relevant variables affecting auction strategy and models them in causal connections using history data in order to simulate the bidding behavior. We verified the effective necessaries of these predictions using empirical auction data, and our result indicated that the prediction with Bayesian Network produce close-to-reality results.