User loss prediction method and system based on cellphone game store

Abstract

The invention provides a user loss prediction method and system based on a cellphone game store, and the method comprises the following steps: collecting the basic information, behavior information and game information of users from a log of a server, and dividing the users into a training set user and a prediction set user; building a loss user tag for the training set user, and carrying out thepreprocessing of original data; carrying out the feature extraction, selection and standardization of the basic information, behavior information and game information of the training set user and theprediction set user; training a gradient increasing decision-making algorithm to obtain a user loss prediction model according to the features and loss user tag of the training set user; and recognizing a loss user of the cellphone game store through the user loss prediction model according to the features of the prediction set user. According to the invention, the method can quickly and accurately recognize the potential loss users based on the business scene of the cellphone game store, and provides a decision making support for the cellphone game store for the timely recall of the loss users.

Description

technical field [0001] The invention relates to the technical field of network data mining, in particular to a user loss prediction method and system based on a mobile game store. Background technique [0002] In recent years, with the popularization and development of mobile communication devices, the mobile game market has continued to grow steadily and rapidly around the world. Mobile game stores, as the entrance to mobile games for users, have always been the strategic deployment center for mobile game merchants. Under this premise, the competition in the mobile game store industry is fierce, and each mobile game store is facing a severe situation of user loss; moreover, retaining existing users can often generate greater profits than introducing new users. Therefore, for the increasingly saturated mobile game store industry, establishing an effective user loss prediction and analysis mechanism can provide decision support for mobile game store user retention, and even ...

AI Technical Summary

Problems solved by technology

Although the current research on user churn prediction in game business scenarios has a wide range of categories, and targets game types ranging from massively multiplayer games to casual games, they are all only for the analysis of a single game

However, based on the prediction and analysis of user churn in mobile game stores, it is necessary to study user behaviors under various game types. That is to say, with the addition of the "game" dimension, the complexity of business scenarios and corresponding feature engineering and the difficulty of modeling are greatly increased.

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