Research On Personalized Content Recommendation Of Mobile Games Users Based On Persona Technology

The rapid development of chip miniaturization technology makes mobile devices more and more powerful.Besides being able to complete the basic functions of various everyday applications,it can also support the smooth running of various types of mobile games,and various types of mobile devices have gradually become commonplace.An important choice for entertainment.Game developers have started mass production of mobile games,and the proportion of mobile game stations is still increasing.How to set up a mobile game player model to subdivide customers has become an important issue in the industry.How to improve the player's opponent's stickiness and pay rate and other indicators is the ultimate appeal of various mobile game operators.Through the research on the personalized content recommendation of mobile game users based on user portraits,game operators can help targeted operators do more accurate marketing during the operation phase.Up Up to now,there have been many researches on PC-side games at horme and abroad.However,through a considerable amount of literature research,it has been found that there is relatively little research on modeling and research for mobile game users.Therefore,for the purpose of precise marketing,this article first analyzes the related portrait technology needed for mobile game user modeling,and studies techniques such as the construction of the player's portrait to provide the player with personalized game scene recommendations.Afterwards,he studied the techniques required for game props recommendation,and finally made personalized recommendations for players in terms of scenarios and props.This article interprets business strategies and strategies to determine the role of user portraits in precision marketing.On this basis,a personalized recommendation model was established using K-means algorithm and collaborative filtering algorithm.When a player plays a game,the precise marketing of the game according to the model will satisfy the customer's demand,and the corresponding player will spend more time and money in the game,and the operator's profit will increase.This paper chooses the K-means algorithm and the collaborative filtering algorithm to perform statistics and analysis on the real data of a game,and through the calculation of a custom recommendation model,the following conclusions are finally drawn:The mobile phone game personalized recommendation system proposed in this paper is compared with the operator The unified marketing of users can greatly improve the stickiness of users.Finally,on the basis of research,we will strive to provide better game marketing recommendations for mobile game developers and operators.