Application recommending method and system based on user portrait behavior analysis, storage medium and computer device

Abstract

The invention provides a game recommending method and system based on user portrait behavior analysis. A feature acquirer is constructed to process user portrait data, application list data and client-reported data to obtain regular feature vectors meeting mathematical modeling requirements; various basic recommending models are used to make predictions to generate a primary user application recommendation list and corresponding download probabilities; a final application recommendation list is generated by combining the download probabilities and an actual label training fusion model. User historical behavior logs are subjected to multidimensional analysis to perform feature extraction so as to construct a user portrait data warehouse. A long and short-term memory network is introduced to the basic recommending models to learn time-series relationships of user behaviors, users' degrees of preference for objects are better depicted, and recommended game applications match well with the needs of users. Integrated learning is added to perform model fusion, learning results of the models are integrated, and accordingly, the stability and generalization ability of a recommending algorithm are improved.

Description

technical field [0001] The invention belongs to the field of network information technology, and in particular relates to an application recommendation method based on user portrait behavior analysis, an application recommendation system based on user portrait behavior analysis, a computer-readable storage medium, and a computer device. Background technique [0002] In recent years, with the rapid development of the mobile Internet industry, the amount of information carried by the Internet has also shown explosive growth. All kinds of mobile Internet information carriers provide users with a variety of ways to obtain information content, but it also brings information overload. It also increases the cost for users to obtain information accordingly. In specific business scenarios, combined with analysis of user behavior habits, providing content and services that meet user preferences has become the core requirement. In order to solve user needs, recommendation methods and ...

AI Technical Summary

Problems solved by technology

[0005] However, the data sources of the above models are relatively single. In different contexts, there will be disadvantages of poor stability and poor generalization ability, which will reduce the accuracy of application recommendation results.

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