000000055 001__ 55 000000055 005__ 20240801202640.0 000000055 02470 $$ahttps://doi.org/10.3886/k3qx-xz33$$2DOI 000000055 037__ $$aADMIN 000000055 245__ $$aFair Notification Optimization: An Auction Approach 000000055 251__ $$av1.1 000000055 269__ $$a2023-09-15 000000055 336__ $$aDataset 000000055 510__ $$aKroer, Christian, Sinha, Deeksha, Zhang, Xuan, Cheng, Shiwen, and Zhou, Ziyu. Fair Notification Optimization: An Auction Approach. Inter-university Consortium for Political and Social Research [distributor], 2023-09-15. https://doi.org/10.3886/k3qx-xz33 000000055 520__ $$aNotifications are important for the user experience in mobile apps and can influence their engagement. However, too many notifications can be disruptive for users. In this work, we study a novel centralized approach for notification optimization, where we view the opportunities to send user notifications as items and types of notifications as buyers in an auction market. <br><br> <p>The full dataset, instagram_notification_auction_base_dataset.csv, contains all generated notifications for a subset of Instagram users across four notification types within a certain time window. Each entry of the dataset represents one generated notification. For each generated notification, we include some information related to the notification as well as information related to the auctions performed to determine if the generated notification can be sent to users. See the README file for detailed column decriptions. The dataset was collected during an A/B test where we compare the performance of the first-price auction system with that of the second-price auction system.</p> <p>The two derived datasets can be useful to study fair online allocation and Fisher market equilibrium. See the README for details and a link to the scripts that generate the derived datasets.</p> 000000055 540__ $$aThis dataset has two levels of access: Public and Login Required. <ul> <li>Public files can be downloaded directly from the dataset record. Typically, documentation files such as READMEs and codebooks are made public.</li> <li>Login Required files can be downloaded directly from the dataset record, once you have created and logged in to your SOMAR account. By downloading data files you agree to <a href="https://socialmediaarchive.org/pages/?page=Terms%20of%20Use&ln=en">ICPSR’s Terms of Use</a>.</li> </ul> 000000055 650__ $$asocial media 000000055 650__ $$amarkets 000000055 655__ $$aweb platform data 000000055 720__ $$aKroer, Christian$$eResearcher$$uColumbia University$$3christian.kroer@columbia.edu$$7Personal 000000055 720__ $$aSinha, Deeksha$$eResearcher$$uMeta Platforms, Inc.$$3deekshasinha@meta.com$$7Personal 000000055 720__ $$aZhang, Xuan$$eResearcher$$uMeta Platforms, Inc.$$3xuanzhang816@gmail.com$$7Personal 000000055 720__ $$aCheng, Shiwen$$eResearcher$$uMeta Platforms, Inc.$$3cheng.shiwen324@gmail.com$$7Personal 000000055 720__ $$aZhou, Ziyu$$eResearcher$$uMeta Platforms, Inc.$$3ziyu@meta.com$$7Personal 000000055 791__ $$tFair Notification Optimization: An Auction Approach$$aJournal Article$$eIs Derived From$$2arXiv$$whttps://arxiv.org/abs/2302.04835 000000055 8564_ $$yinstagram notification auction dataset$$91b86bcdd-25b0-4dda-88e3-3b2727988469$$s51401577$$uhttps://socialmediaarchive.org/record/55/files/instagram_notification_auction_base_dataset.csv 000000055 8564_ $$yinstagram notification auction dataset$$9df29e092-def3-4e86-b6de-338b1bfb0e6c$$s5520707$$uhttps://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_one_day_window.csv 000000055 8564_ $$yinstagram notification auction dataset$$9d3b9b3aa-64c9-4bde-8bcf-2605bf357914$$s7527757$$uhttps://socialmediaarchive.org/record/55/files/instagram_notification_auction_derived_dataset_two_day_window.csv 000000055 8564_ $$yREADME documentation$$98338b32a-00bb-4f69-8bac-5a43a2cf4632$$s4189$$uhttps://socialmediaarchive.org/record/55/files/README.txt 000000055 906__ $$a2022$$b2023 000000055 908__ $$aInstagram 000000055 923__ $$ahttps://github.com/facebookresearch/FairNotification 000000055 924__ $$aImplementation of algorithms for auction-based notifications and code to generate derived datasets appearing in paper "Fair Notification Optimization: An Auction Approach" 000000055 926__ $$aPython 000000055 926__ $$aJulia 000000055 927__ $$dJupyter Notebook 000000055 980__ $$aDatasets 000000055 980__ $$aInstagram 000000055 981__ $$aPublished$$b2023-09-15