Mobile phone game recommendation method based on binary decision tree

Abstract

The invention discloses a mobile phone game recommendation method based on a binary decision tree, mainly trains a binary decision tree classification model to determine whether a user is interested in a mobile phone game to be recommended or not so as to transform influence on recommendation by characteristic weight into the automatic prediction of a learning model from subjective assignment. The mobile phone game recommendation method has the characteristics that the binary decision tree is used for determining the characteristic selection and weight measurement problems in a game recommendation scene, and a corresponding recommendation model is given. The mobile phone game recommendation method can be used for favorably guaranteeing the accuracy of a recommendation result and user preference, and meanwhile, the diversity of the recommendation result can be guaranteed on the premise that the recommendation result does not need to be additionally subjected to second pickup.

Description

technical field [0001] The invention relates to the fields of data mining and user behavior prediction, in particular to a mobile game recommendation method based on a binary decision tree. Background technique [0002] Today, with the rapid development of China's economy, Internet and entertainment industry, more and more people pay attention to the quality of leisure time. The development of smart phones has also brought about the rapid development of mobile application software, mobile advertising and mobile game industry. Due to the diversity of game types and adaptation levels, a large number of mobile games will also bring information overload to users. Therefore, the method of game recommendation for mobile phone users has also emerged as the times require. [0003] Game recommendation is one of the extended application forms of recommendation technology research. A large number of games have created a demand for recommendations, and the recommended prediction resul...

AI Technical Summary

Problems solved by technology

Applying the core principles of classic recommendation technology, such as content-based recommendation and collaborative filtering-based recommendation, will lead to some problems: 1) Content-based recommendation can easily make the games that users get too concentrated, which is not conducive to the promotion of new games

2) The recommendation based on collaborative filtering cannot update the user preference model in time

3) Traditional recommendation methods do not consider specific applications in detail, such as the features and their weights in game recommendation

4) Existing methods do not consider the game recommendation expansion problem

That is, the recommendation model is not universally applicable and cannot adapt to changes in recommendation conditions in recommendation scenarios

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