Optimizing Strategies For Mobile Video Platforms Using Personalized Recommendation

With the development of Internet technology and mobile terminals,the boom of big data technology has made personalized recommendations achieve a good recommendation effect that the Internet could not do in the early days.The number of mobile video platforms that use personalized recommendation technology has also increased,and people's lifestyles and their ways of entertainment have changed.People have become accustomed to using mobile devices for video creation.It has never been so easy to become a video creator.The surge in creators has resulted in the explosive growth of platform content.How to recommend personalized video content to the right users in a limited information space is a key issue that every video platform is facing.Creators of mobile video platforms using personalized recommendation technology have formed unique creating habits,while different users have formed different viewing habits.The research object of this paper is the mobile video platform with personalized recommendation.In the experimental stage,two platforms,Bilibili and Tik Tok,were selected for experiments.Firstly,starting with the background and significance of the topic,this paper discusses the current situation and popular reasons for the video platform using personalized recommendation mechanism.To tease out the impact of the personalized recommendation of the mobile video platform on the creators and users behavior habits,the creators begin to have creative behavior habits of deconstructing the video into tags lowering the attitude and interact with the users,maintaining the audience-given design,using pay promotion services.The users appear consumer behaviors of habitual passive,fast-consuming platform content,interacting with creators and platforms and monitors platform content,accepting vertical format video,and this paper analyzes internal and external causes of such changes.Through experiments,we quantify the influence of the platform on which certain behavior habits of users will be encountered,discover problems and build a platform model.Combined with user behavior analysis,the mobile video platform with personalized recommendation has four independent features,such as strong feedback,strong guidance,non-equalization,and cultural circle segmentation,and the possible crisis caused by this feature.Finally,combined with the characteristics of the platform model and such crises,the corresponding improvement schemes are proposed from three perspectives of creators,platforms and regulatory authorities: Creators establish user privacy model;platform publishes and maintains public information objectively and regularly;platform rationally differentiates creator behavior;platform helps users improve media literacy;platform provides diversified content delivery ratio;the regulatory authorities force the platform to publish the main rules of recommendation algorithm,and add offset algorithm to user data and other optimization strategies.At present,the mobile video platform based on the personalized recommendation is still in the development stage.The technology is not yet mature and the law is not perfect.I hope this paper can provide references for mobile video platforms with the personalized recommendation which still in the development stage in the future.