Name
Abstract | ||
|---|---|---|
Notifications (including emails, mobile / desktop push notifications, SMS, etc.) are very effective channels for online services to engage with users and drive user engagement metrics and other business metrics. One of the most important and challenging problems in a production notification system is to decide the right frequency for each user. In this paper, we propose a novel machine learning approach to decide notification volume for each user such that long term user engagement is optimized. We will also discuss a few practical issues and design choices we have made. The new system has been deployed to production at Pinterest in mid 2017 and significantly reduced notification volume and improved CTR of notifications and site engagement metrics compared with the previous machine learning approach.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3219819.3219906 | KDD |
Keywords | Field | DocType |
Notification Volume Optimization,Machine Learning | Push technology,World Wide Web,Business metrics,Volume control,Computer science,User engagement,Communication channel,Notification system,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5552-0 | 2 | 0.38 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Zhao | 1 | 969 | 36.08 |
| Koichiro Narita | 2 | 2 | 0.38 |
| Burkay Orten | 3 | 98 | 6.00 |
| John Egan | 4 | 2 | 0.38 |