Find crucial factors of in-game purchase using neural networks

Chen, Long-Sheng; Lin, Meng-Ru; Pan, Yi-Ting

doi:10.1109/icawst.2017.8256473

Cited by 1 publication

(2 citation statements)

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“…Collaborative filtering does have a higher accuracy and is being developed based on deep learning methods (Chen, Lin, and Pan, 2017). This approach faces another challenge -the problem of scalability.…”

Section: Proof Of Concept Algorithm Developmentmentioning

confidence: 99%

“…New models on top of costly methods used in collaborative filtering introduced the time cost of using neural networks. The solution to this challenge with new methods of sampling was presented in research (Chen, Lin, and Pan, 2017). In the work of Weia et al (2017) an approach of solving cold start (players without historical data) challenge has been proposed.…”

Section: Proof Of Concept Algorithm Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Discovery and Framework for Purchase Behavior Analysis in Mobile Gaming Applications

2020

14th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, 5th International Conf

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The gaming industry is a fast expanding industry with a large global market and is projected to hit $300 billion by 2025. The total number of players in 2019 was projected to hit 2.4 billion (Taylor, 2019). Knowledge discovery is achieved by collecting and analyzing data from within the gaming applications. The data collected is used to understand players, gain insights and to improve products for the gaming community based on feedback and user interaction. And as a result, obtained knowledge contributes to monetization in a way that is especially interesting in the e-sports and streaming space. Machine learning is one of the technologies that can assist in knowledge discovery as it provides potential to obtain insights into previously overlooked data. This paper provides insight in a work of how machine learning algorithms are applied to gain behavior understanding whithin mobile gaming applications in a way compliant with European General Data Protection Regulation (GDPR). The paper is a part of a larger research work and contributes to the domain of knowledge discovery within in-app purchase behavior data and serves as a step towards further research in this area.

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Section: Proof Of Concept Algorithm Developmentmentioning

confidence: 99%

Section: Proof Of Concept Algorithm Developmentmentioning

confidence: 99%