Abstract
This disclosure analyzes the background of the industry and proposes solutions to the challenges faced by complex applications or games, such as strategy games. From the perspective of the system architecture, this disclosure describes how to clean up data, identify salient features, model predictive classifiers, and automate analysis and selection for content delivery. Data collection and processing have a great influence on the accuracy and applicability of the model. Four kinds of behavioral parameters (or more) may be used to predict conversion events, with PCA used to reduce the dimensions utilized as inputs to the model. In addition, by adjusting the threshold of the predicted conversion probability, a trade-off can be made between accuracy and breadth, so that the prediction results of the model can be applied to different fields.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Li, Leon, "Machine Learning Prediction System Based on Tensor-Flow Deep Neural Network and its Application to Advertising in Mobile Gaming", Technical Disclosure Commons, (April 27, 2018)
https://www.tdcommons.org/dpubs_series/1175