Joint Optimization of Multiple Performance Metrics in Online Video Advertising
Autor: | Sergey Faleev, Jianqiang Shen, Santanu Kolay, Sean O'Donnell, Sahin Cem Geyik |
---|---|
Rok vydání: | 2016 |
Předmět: |
Multimedia
Share of voice business.industry Computer science Display advertising Brand awareness Contextual advertising Advertising 02 engineering and technology computer.software_genre Online advertising Advertising research Advertising campaign 020204 information systems Search advertising 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Banner business Native advertising computer |
Zdroj: | KDD |
Popis: | The field of online advertising, in essence, deals with the problem of presenting ads to online users in the most appropriate contexts to achieve a multitude of advertiser goals. A vast amount of work in online advertising has been focused on optimizing banner display advertising campaigns where the main goal lies in direct response metrics, often as clicks or conversions. In this paper, we explore the newly popularized space of online video advertising, where brand recognition is the key focus. We propose a framework based on a feedback mechanism where we optimize multiple video specific performance indicators while making sure the delivery constraints (budget and user reach) of advertisers are satisfied. While our main focus is on improving metrics such as engagement (amount of view time), and viewability (whether a campaign is within eyesight of a user), we also discuss the possibilities of expanding to other metrics. We demonstrate the benefit of our framework via empirical results in multiple real-world advertising campaigns. To the best of our knowledge, this is the first paper that deals with the unique challenges arising from the nature of online video advertising. |
Databáze: | OpenAIRE |
Externí odkaz: |