TY - GEN AB - 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. <br><br> <p>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.</p> <p>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.</p> DA - 2023-09-15 ED - Kroer, Christian ED - Sinha, Deeksha ED - Zhang, Xuan ED - Cheng, Shiwen ED - Zhou, Ziyu ED - Researcher ED - Researcher ED - Researcher ED - Researcher ED - Researcher ID - 55 KW - social media KW - markets L1 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv L1 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv L1 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv L1 - https://socialmediaarchive.org/record/55/files/README.txt L2 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv L2 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv L2 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv L2 - https://socialmediaarchive.org/record/55/files/README.txt L4 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv L4 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv L4 - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv L4 - https://socialmediaarchive.org/record/55/files/README.txt LK - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv LK - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv LK - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv LK - https://socialmediaarchive.org/record/55/files/README.txt N2 - 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. <br><br> <p>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.</p> <p>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.</p> PY - 2023-09-15 T1 - Fair Notification Optimization: An Auction Approach TI - Fair Notification Optimization: An Auction Approach UR - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv UR - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv UR - https://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv UR - https://socialmediaarchive.org/record/55/files/README.txt Y1 - 2023-09-15 ER -