Name
Abstract | ||
|---|---|---|
Electronic publishers and other web-companies are starting to collect user feedback on ads with the aim of using this signal to maintain the quality of ads shown on their sites. However, users are not randomly sampled to provide feedback on ads, but targeted. Furthermore some users who provide feedback may be prone to dislike ads more than the general user. This raises questions about the reliability of ad feedback as a signal for measuring ad quality and whether it can be used in ad ranking. In this paper we start by gaining insights to such signals by analyzing the feedback event logs attributed to users of a popular mobile news app. We then propose a model to reduce potential biases in ad feedback data. Finally, we conclude by comparing the effectiveness of reducing the bias in ad feedback data using existing ad ranking methods along with a new and novel approach we propose that takes revenue considerations into account.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308560.3317304 | Companion Proceedings of The 2019 World Wide Web Conference |
DocType | ISBN | Citations |
Conference | 978-1-4503-6675-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Bron | 1 | 5 | 1.11 |
| Ke Zhou | 2 | 30 | 1.75 |
| Andy Haines | 3 | 5 | 0.77 |
| Mounia Lalmas | 4 | 2593 | 225.26 |