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Aiming at the information overload caused by rich resources of Broadcast Television programs, this paper puts forward Broadcast Television Programs Recommendation Technology based on user clustering. According to user rating data and programs broadcasting data, we cluster users by the improved K-MEANS algorithm, divide the users with similar viewing preference into the same community groups, and generate...
Camouflage plays a key role in the success of the war. In this paper, we discuss how to give effective camouflage for disguise in the specific terrain, and present a method which different from the traditional k-means clustering. In general, camouflage consists of several blocks. Firstly, this paper combines the method of probability with statistics to analyze the colors from different pictures of...
Initialization sensitivity usually occurs in dictionary learning algorithm for image decomposition. In this paper, we propose an adaptive dictionary learning algorithm by promoting structural incoherence at the stage of dictionary updating. The structural incoherence based dictionary learning (SIDL) method guides the cartoon and texture parts to be more properly represented by two incoherent dictionaries...
With the rapidly increasing implementation and popularity of broadband new media platforms in recent years, the effective personalized content service model has become a key issue in the establishment and development of New Media. In this paper we propose a user preference clustering framework for the customization of content service in broadband new media platforms, i.e. IPTV, digital TV, video portals...
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