3/15/2023 0 Comments Tv mcr ch timetag![]() The simulations also verify the convergence and effective of this algorithm.ĭigitalization of television broadcasting results in an increasing number of broadcast channels and is making general viewers difficult to do program select operations. It's proved that the result of products recommending of this algorithm converges to the result of products sorting with “real” customer preference matrix. This products recommending algorithm recommends some products dynamically to customer, re-computing customer preference matrix with customer affective evaluation to products recommended, then go to the next iteration. On these bases, the complete products recommend algorithm base on customer preference model and affective computing is proposed. The degree of customer preference to each value of each product feature is well described by a value which is similar to membership in fuzzy space, and method of match degree computing between feature of product and preference of customer is proposed. The model of customer preference base on feature space of products has been build by introducing the concept of affective computing. Products recommending on personalized preference of customer is one kind of effective product recommend algorithm base on contents, in which the difficulties are uncertainty and descriptive fuzziness of customer preference modeling. Our results indicate that the proposed scheme substantially outperforms the current Video‐on‐Demand distribution mechanisms in terms of network bandwidth consumption, significantly reducing operation costs by improving the system scalability. The efficiency of the proposed method is demonstrated through computational simulations of different scenarios, including a sample of real users' activity. This paper presents a new method for Video‐on‐Demand distribution that explores the client storage capabilities by modelling the users' preferences with a Hidden Markov Model. The most efficient methods available for Video‐on‐Demand distribution use strategies that combine multicast transmission and the storage capacity of the user's equipment. The efficiency of distribution is strongly related to the operating costs of the providers of such services. In this scenario, Video‐on‐Demand services require that distribution mechanisms improve the efficiency of video transmission, which impacts the network performance and system scalability. ![]() ![]() The quality of transmitted videos and the number of content available online is expected to increase in the upcoming years, which will further pressure the available network infrastructures. Internet traffic is already dominated by video streaming applications.
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