Research And Implementation Of Traffic Classification Based On Deep Flow Inspection

With the popularity of Internet techonology applications,the exponential growth trend in the number of flooding the network resources.The flood of information presented to the user at the same time,the "imformation explosion" and "imformation overload" phenomenon.The personalized recommendation system is an information services technology to alleviate this problem,it is based on user history and behavior information to build user interest model recommended by the information the may be of interest to the user through the model.Recommendation system on the one hand in the massive amounts of data by predictiong user preference of the project to provide users with information filtering,application of knowledge discovery technology to generate personalized recommendation to help users find the information they need;other hand auxiliary enterprises achieve personalized marketing purpose,and thus increase sales,create more profits for the enterprise. At present, the research direction of the domestic and foreign scholars has become an important research direction.In order to provide the recommendation, this paper makes an exploratory research on the combination recommendation algorithm, and applies it to the intelligent recommendation system of the Cloud Media TV in JSCN. The main contents are as follows:(1) This paper studies the common recommendation algorithm in intelligent recommendation system.The different kinds of recommendation algorithms are summarized and compared, and the disadvantages and shortcomings of the current recommendation algorithm are summarized in detail.(2) Design a new personalized combination algorithm. In view of the present traditional on-demand rankings, collaborative filtering single recommendation algorithm has obvious deficiencies in this situation, this paper proposes and designs a new combination of personalized recommendation algorithms, the algorithm in the rankings on demand algorithm and collaborative recommendation algorithm based on improved, with better recommendation results. The algorithm first according to the video programs on demand historical data were film heat calculation, and between the user interests and behaviors based on similarity to build user preference relationship network. Through the combination of the two, the similarity between users; finally, through for users to personalize recommendation on demand ranked higher video program.(3) Development and design of the intelligent recommendation system for the Cloud Media TV. This paper studied Cloud Media TV recommend recommendation system metadata extraction, enhancement mechanism, realization of video metadata database establishment and enhanced; and by building a recommendation engine, outward flexible video recommendation ability. The proposed personalized recommendation algorithm based, design and implementation of the a personalization recommendation based on video intelligent recommendation system, and has carried on the application in the "JSCN Cloud Media TV ".