Research On Vehicle Multimedia System And Content Recommendation

With the popularity of high-speed railway in China,more and more users choose high-speed rail travel.In the long journey,passengers have a wide range of demand for multimedia services.However,there is a lack of mature models for users to provide multimedia services in the special scene of high-speed railway.It has a great relationship with the particularity of high-speed rail.First high-speed rail train is a closed car,the signal shielding effect is very strong.Therefore,single point wireless AP can't cover all the carriages of the entire train.Second trains are not allowed to be modified after they leave factory because of the reasons for the safety.So we can't install wired net on the train which has been left the factory.Through the in-depth discussion on the particularity of high-speed train,the author designs and realizes the distributed vehicle multimedia operation and maintenance system according to train's networking characteristics.This system is divided into three modules: audio and video system module,the content recommended module and Operation-maintenance module.For the realization of audio-visual system,the author first analyzed the main program that achieved the current streaming media technology then chose the most cost-effective solution.Audio and video system module is through the XAMPP service pack,HTML5,JAVASCRIPT,CSS technology and HTTP protocol,to achieve the client's audio and video playback.Operation and maintenance module through JAVA technology and SOCKET communication to achieve a number of distributed servers and system maintenance between the information transmission.Content recommendation module is the focus of this article.Through the collaborative filtering recommendation algorithm,it can recommend users to their favorite program content according to their historical browsing data.At the same time,the author optimizes the recommendation algorithm according to the particularity of the scene.And the author improves the recommendation system's accuracy in the sparse user data and too little movie reviews and provides a better service for the users.