Research On Personalized Recommendation Of News Aggregation APP

Technological progress has profoundly changed the form of media,the mode of production and distribution of content.The main information distribution mode is to support the recommendation engine based on user interest with big data and algorithm.In China,represented by "today's headline" flagship news aggregation of personalized recommendation APP,the user scale,market share and user activity,etc.,beyond the represented by the tencent news portal news APP,subversion spread pattern,become the focus of attention of industry.This paper takes news aggregation APP as the main research object,and carries on the research with personalized recommendation.First of all,from the connotation,emergence and development of news aggregation APP,the news APP and the news aggregation APP are differentiated,and the development overview of news aggregation APP is outlined at the macro level.Secondly from locking recommended recommend goals,laying turf,build recommended skeletons,achieve one thousand thousand surface touch up to four levels,focused on the implementation of the personalized recommendation news aggregation APP,this is the focus of this article.On the basis of this,it is difficult to analyze the personalized recommendation of news aggregation APP,including user's supreme thought,technical centralism tendency and traffic flow as king orientation.Although the main personalized recommendation news aggregation APP guy in the world scope,but generally presents the algorithm under the scrutiny of user narrow field of vision,user privacy,value orientation error and so on.As an information distribution platform,news aggregation APP should assume more responsibilities and obligations in information dissemination.The realization of the transformation from "the efficiency of information dissemination" to "the value of information transmission",from "user first thinking" to "user value uppermost thinking".Stick to the right value orientation and commit to disseminating valuable information.This article will combine the writer in "today's headlines and tencent news internship experience,from multiple combined with common change to ensure that the personalized content,introducing third-party data regulators to prevent risks,set up rules to the social benefit optimization Suggestions.