Cross-device matching approaches: word embedding and supervised learning

Author:

Lin Frank Yeong-Sung,Hsiao Chiu-HanORCID,Zhang Si-Yuan,Rung Yi-Ping,Chen Yu-Xuan

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Reference25 articles.

1. Karakaya, C., Toğuç, H., Kuzu, R.S., Büyüklü, A.H.: Survey of cross device matching approaches with a case study on a novel database. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 139–144 (2018). https://doi.org/10.1109/UBMK.2018.8566307

2. Digital Omnivores: How Tablets, Smartphones and Connected Devices are Changing U.S. Digital Media Consumption Habits. https://www.comscore.com/Insights/Presentations-and-Whitepapers/2011/Digital-Omnivores. Accessed March 16 2021

3. Facebook Business: Measuring Conversions on Facebook, Across Devices and in Mobile Apps. https://www.facebook.com/business/news/cross-device-measurement. Accessed: March 16 2021

4. Google Help Center: Limits of User-ID views & Cross Device reports. https://support.google.com/analytics/answer/3223194?hl=en. Accessed March 16 2021

5. Buss, J.: Cross-device advertising: how to navigate mobile marketings next big opportunity. J. Digital Soc. Med. Market. 3(1), 73–79 (2015)

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1. Multi-View User Representation Learning for User Matching Without Personal Information;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

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