nach oben

Soft Computing

Erschienen in:

04.01.2019 | Methodologies and Application

C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation

I know you’ll click

verfasst von: TonTon Hsien-De Huang, Hung-Yu Kao

Erschienen in: Soft Computing | Ausgabe 22/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

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.

Vorheriger Artikel A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees

Nächster Artikel Biometric iris recognition using radial basis function neural network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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, Savannah

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 273–280

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. Boston

Covington P, et al (2016) Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. Boston

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. An MIT Press Book, CambridgeMATH

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 Vegas

Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37CrossRef

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 1097–1105

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRef

Pan R, Zhou Y, Cao B, Liu N, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In ICDM, pp 502–511

Salakhutdinov R, Mnih A, Hinton GE (2007) Restricted Boltzmann machines for collaborative filtering. ICML, pp 791–798

Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489CrossRef

Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations 2015 (ICLR2015), San Diego

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), WA

Van Den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. NIPS, pp 2643–2651

Verstrepen K, Goethals B (2014) Unifying nearest neighbors collaborative filtering. In: Proceedings of the 8th ACM conference on recommender systems. ACM, New York, pp 177–184

Wang X, Wang Y (2014) Improving content-based and hybrid music recommendation using deep learning. ACM Multimedia, pp 627–636

Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. KDD, pp 1235–1244

Wu Y, DuBois C, Zheng AX, Ester M (2016) Denoising auto-encoders for top-N recommender systems. WSDM, pp 153–162

Zheng Y, Tang B, Ding W, Zhou H (2016) A neural autoregressive approach to collaborative filtering. arXiv:1605.09477

Titel: C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation
I know you’ll click
verfasst von: TonTon Hsien-De Huang
Hung-Yu Kao
Publikationsdatum: 04.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 22/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-03730-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 22/2019

An improved rough set approach for optimal trust measure parameter selection in cloud environments

An efficient character recognition method using enhanced HOG for spam image detection

Impulse noise removal by an adaptive trust-region method

Meta-heuristic algorithms for a clustering-based fuzzy bi-criteria hybrid flow shop scheduling problem

Forecasting stock volatility process using improved least square support vector machine approach

Design of a hierarchy modular neural network and its application in multimodal emotion recognition