Design And Implementation Of Real-time Game User Profile System Based On Big Data

The development of the Internet has provided more choices for people's lifestyles,including more and more people choosing to play video games as a form of leisure and entertainment.Therefore,game promotion platforms like Steam and 4399 have also emerged.These platforms are designed to provide users with more interesting games,so as to help platforms and game developers get greater benefits.Game users will generate a lot of data when using these platforms.With the widespread application of big data technology,game promotion platforms will also build user profile systems,by collecting,storing and analyzing user data to help the platform attract more users.Most of the existing game user profile systems on the market are mainly offline data analysis.This type of system is characterized by offline batch processing.This data processing method is mainly suitable for situations that do not require high real-time performance.However,in reality,user needs always change irregularly.Therefore,a high real-time game user profile system can help companies discover changes in user needs in a timely manner and then quickly adjust business strategies to meet the user needs.In order to improve the real-time performance of the game user profile system,in this paper,the core technology of processing data with Flink stream is used to design and implement a real-time game user profile system based on big data.The design and realization of the whole system mainly includes the following contents: user data design,collection,storage,processing and profile result display.According to the data source,data volume and data update frequency,the user data is designed,and the data is divided into two categories: static data and dynamic data.Data collection is mainly done using distributed message queue Kafka.The persistent storage of data is realized by using the log collection tool Flume.Data processing uses stream computing with the characteristics of real-time computing and low latency,which is mainly realized by distributed computing engine Flink.Moreover,TF-IDF algorithm is applied to realize user tag profile,and K-Means algorithm is applied to complete user group classification.The final profile result display is a web page system developed by using Vue and High Charts technology,so that the decision-making layer of the game promotion platform can more intuitively observe the changes in user needs.The use of a real-time game user profile system based on big data will provide game promotion platforms with high-real-time user profile for decision-making analysis,and ultimately help the platform achieve further development and best benefits.