| Summary: | In 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks the input of temporal variations in user data and the graph-based analysis of user relationship characteristics, resulting in a poor prediction effect. In this paper, a neural-network-based approach has been applied to address the limitation, whereby user data do not feature temporal variation. Furthermore, innovative approaches have been proposed to construct multilayer community networks through users’ geographic attributes and to analyze community networks with a network embedding method based on the matrix factorization framework. This fills a gap in existing research areas, whereby the geographic attributes of users have not received much attention. Considering the extensive inputs and multiple features of the predicted attributes, in this paper, the strengths and weaknesses of three feature selection methods are compared, as well as the prediction accuracy of each of the five prediction models. Finally, the embedded feature selection method, deep neural network model, and the Light GBM model are shown to provide better results. After introducing the user community network, it was found that the prediction evaluation indicators of both the deep neural network model and the Light GBM model are improved.
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