C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation
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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.
KeywordsClick sequence aware Deep learning Deep neural network
This work would not have been possible without the valuable dataset offered by Cheetah Mobile Inc.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J et al (2016) TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, SavannahGoogle Scholar
- Aiolli F (2013) Efficient top-n recommendation for very large scale binary rated datasets. In: Proceedings of the 7th ACM conference on recommender systems. ACM, New York, pp 273–280Google Scholar
- Cheng H-T, et al (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. BostonGoogle Scholar
- Covington P, et al (2016) Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. BostonGoogle Scholar
- He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), Las VegasGoogle Scholar
- Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems 25 NIPS. Harrahs and Harveys, Lake Tahoe, pp 1097–1105Google Scholar
- Pan R, Zhou Y, Cao B, Liu N, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In ICDM, pp 502–511Google Scholar
- Salakhutdinov R, Mnih A, Hinton GE (2007) Restricted Boltzmann machines for collaborative filtering. ICML, pp 791–798Google Scholar
- Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations 2015 (ICLR2015), San DiegoGoogle Scholar
- Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), WAGoogle Scholar
- Van Den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. NIPS, pp 2643–2651Google Scholar
- Verstrepen K, Goethals B (2014) Unifying nearest neighbors collaborative filtering. In: Proceedings of the 8th ACM conference on recommender systems. ACM, New York, pp 177–184Google Scholar
- Wang X, Wang Y (2014) Improving content-based and hybrid music recommendation using deep learning. ACM Multimedia, pp 627–636Google Scholar
- Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. KDD, pp 1235–1244Google Scholar
- Wu Y, DuBois C, Zheng AX, Ester M (2016) Denoising auto-encoders for top-N recommender systems. WSDM, pp 153–162Google Scholar
- Zheng Y, Tang B, Ding W, Zhou H (2016) A neural autoregressive approach to collaborative filtering. arXiv:1605.09477