
@article{ADMIN,
      recid = {55},
      title = {Fair Notification Optimization: An Auction Approach},
      address = {2023-09-15},
      number = {ADMIN},
      abstract = {Notifications are important for the user experience in  mobile apps and can influence their engagement. However,  too many notifications can be disruptive for users. In this  work, we study a novel centralized approach for  notification optimization, where we view the opportunities  to send user notifications as items and types of  notifications as buyers in an auction  market.
&lt;br&gt;&lt;br&gt;
&lt;p&gt;The full dataset,  instagram_notification_auction_base_dataset.csv, contains  all generated notifications for a subset of Instagram users  across four notification types within a certain time  window. Each entry of the dataset represents one generated  notification. For each generated notification, we include  some information related to the notification as well as  information related to the auctions performed to determine  if the generated notification can be sent to users. See the  README file for detailed column decriptions. The dataset  was collected during an A/B test where we compare the  performance of the first-price auction system with that of  the second-price auction system.&lt;/p&gt;

&lt;p&gt;The two derived  datasets can be useful to study fair online allocation and  Fisher market equilibrium. See the README for details and a  link to the scripts that generate the derived  datasets.&lt;/p&gt;},
      url = {http://socialmediaarchive.org/record/55},
}