Name
Abstract | ||
|---|---|---|
In this paper, we describe an exploration system that was implemented by the search-advertising team of a prominent web-portal to address the cold ads problem. The cold ads problem refers to the situation where, when new ads are injected into the system by advertisers, the system is unable to assign an accurate quality to the ad (in our case, the click probability). As a consequence, the advertiser may suffer from low impression volumes for these cold ads, and the overall system may perform sub-optimally if the click probabilities for new ads are not learnt rapidly. We designed a new exploration system that was adapted to search advertising and the serving constraints of the system. In this paper, we define the problem, discuss the design details of the exploration system, new evaluation criteria, and present the performance metrics that were observed by us. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3097983.3098041 | KDD |
Field | DocType | ISBN |
Search advertising,Computer science,Computer security,Impression,Human–computer interaction,Artificial intelligence,Machine learning | Conference | 978-1-4503-4887-4 |
Citations | PageRank | References |
1 | 0.40 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parikshit Shah | 1 | 315 | 18.43 |
| Ming Yang | 2 | 14 | 6.90 |
| Sachidanand Alle | 3 | 1 | 0.40 |
| Adwait Ratnaparkhi | 4 | 1359 | 292.99 |
| Ben Shahshahani | 5 | 1 | 0.40 |
| Rohit Chandra | 6 | 2 | 0.78 |