Abstract
With the rapid development of information especially internet technology, people have to choose the most suitable goods without any experience, so the recommendation system is seriously required. Yet no research on advertisement recommendation system for movie play is presented. Regarding this problem, the paper introduces the theory of semantic computing and annotates the semantic tags from the movie slices and the candidate advertisements, the potential preferences on them are predicted with neutral network model trained by some data set predefined. The user preference model and the predicting workflow are described in detail. Finally, the MovieLens dataset is employed to validate the validity of the system designed. The results of simulation experiments prove that the technology proposed can not only satisfy the requirement of matched advertisement recommendation but also outperform the traditional collaborative filtering algorithm.
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