Research On Behavior Analysis Of Internet Users And Application Based On Data Mining

Internet TV provides users with more choice, while the development of InternetTV is facing a new challenge. On the one hand, many video programs available on theInternet are not completely interested by users. Through multiple browsing andsearching they usually find to watch the videos and meet their own needs. On theother hand, Internet TV operators can’t fully understand the individual needs of theaudiences, because the television audience is a stereo-typed interface, it is unable tomaintain a stable customer relationship. Namely the lack of personalized service isbecoming the key issues constraining the development of Internet TV. This requires asite capable of analyzing users’ preferences, behavior and other information.According to the information provided and automatically recommended services tousers called as intelligent recommendation system,"user-centric, personalizedservice" on the network is proposed. In this background, Internet data mining iscombined with Internet TV. Internet data mining is to find the network from the serverlogs and logs user behavior and to draw interest, potentially useful patterns andpotential information. The traditional data mining technology and the Internet together,can play a role in many aspects, it is a new research direction in the field of datamining.This paper introduced the technology of data mining, analyzed the currentsituation and development trend of Internet TV, a recommended system model isestablished on the demands based on the whole network user viewing behavior data,and the mutual coordination between the various system modules and their functionsare described. The depth study of the recommendation algorithm on Internettelevision recommendation system is applied. Finally, the whole network data CNTVwas realized to collaboratively filter recommendation system based on clusteringalgorithm based on the overall structure and function recommendation algorithmCNTV called as a cloud multi-screen. Intelligent recommendation system, and thesystem in detail introduction are analyze and summarized.