Abstract
With the development of new media, the traditional broadcast television has encountered a tremendous impact. So, the broadcast television industry is actively seeking innovation to break through the dilemma. It is becoming more and more urgent to build a reasonable framework for new broadcast television resource storage and distribution. Facing the deployment of new broadcast television, the primary issue is to realize the cold start based on new programs. This paper uses the clustering algorithm, similarity algorithm, and information entropy algorithm to construct a new broadcast television user interest model, which includes two sub-models, and finally achieves the cold start of programs. Compared with the Item-CF method, as the experimental results show, our new model is better than the traditional collaborative filtering algorithm in some indicators.
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