Name
Abstract | ||
|---|---|---|
In digital advertising, advertisers want to reach the right audience over media channels such as display, mobile, video, or social at the appropriate cost. The right audience for an advertiser consists of existing customers as well as valuable prospects, those that can potentially be turned into future customers. Identifying valuable prospects is called the audience extension problem because advertisers find new customers by extending the desirable criteria for their starting point, which is their existing audience or customers. The complexity of the audience extension problem stems from the difficulty of defining desirable criteria objectively, the number of desirable criteria (such as similarity, diversity, performance) to simultaneously satisfy, and the expected runtime (a few minutes) to find a solution over billions of cookie-based users. In this paper, we formally define the audience extension problem, propose an algorithm that extends a given audience set efficiently under multiple desirable criteria, and experimentally validate its performance. Instead of iterating over individual users, the algorithm takes in Boolean rules that define the seed audience and returns a new set of Boolean rules that corresponds to the extended audience that satisfy the multiple criteria. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783258.2788603 | ACM Knowledge Discovery and Data Mining |
Keywords | Field | DocType |
Online advertising,Targeting,Audience Extension | Multiple criteria,Computer science,Communication channel,Online advertising,Multimedia | Conference |
Citations | PageRank | References |
7 | 0.48 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Shen | 1 | 236 | 17.86 |
| Sahin Cem Geyik | 2 | 115 | 11.71 |
| Ali Dasdan | 3 | 849 | 73.11 |