Research On Personalized Recommendation Ofaggregate News Clients

With the advent of the mobile new media era,mobile news clients have become the major access for people to obtain information.The aggregate news clients represented by Toutiao,Partical News,and the Daily Express aggregate massive information by virtue of their computer algorithm,accurately forecast users' interests and hobbies and quickly gain users' favor by providing personalized recommendations.With application of personalized recommendation technology in news communication,the research on personalized recommendation content certainly will be a new subject in the area.Based on development situation of the aggregate news client,the author uses the relevant theories of communication to analyze the personalized recommendation.Taking three well-developed news clients,Toutiao,Partical News,and the Daily Express as examples,to discuss surface features and deep features of the personalized recommendation.The surface features are its external appearances,including personalized landing pages,their channel settings and content interaction.And deep features are further analysis to the recommendations,including research on the sources,categories,presentations,styles,etc.The personalized recommendation of the aggregate mobile news client uses the user's interest as an "index" to help them quickly get the content they are interested in from massive information.It facilitates users to quickly obtain personalized content in the era of information flooding.However,it has its drawbacks,such as over-entertainment,ignorance of the user's "humanization" needs,and bias in values which have had a certain negative impact on users and society.On this basis,from the perspective of users,operators of aggregated news clients,and relevant government departments,the countermeasures and optimal suggestions for the negative impact of personalized recommendation content are proposed.It is hoped that these researches can playa guiding role in the application of personalized recommendation content in the field of news communication.