With the emergence of mobile and wearable devices, push notification becomes a powerful tool to connect and maintain the relationship with app users, but sending inappropriate or too many messages at the wrong time may result in the app being removed by the users. In order to maintain the retention rate and the delivery rate of advertisement, we adopt deep neural network (DNN) to develop a pop-up recommendation system “Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation (C-3PO)” enabled by collaborative filtering-based hybrid user behavioral analysis. We further verified the system with real data collected from the product security master, clean master, and CM browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In this way, we can know precisely about users’ preference and frequency to click on the push notification/pop-ups, decrease the troublesome to users efficiently, and meanwhile increase the click-through rate of push notifications/pop-ups.
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This work would not have been possible without the valuable dataset offered by Cheetah Mobile Inc.
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Communicated by V. Loia.
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Huang, T.H., Kao, H. C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation. Soft Comput 23, 11793–11799 (2019). https://doi.org/10.1007/s00500-018-03730-5
- Click sequence aware
- Deep learning
- Deep neural network