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Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes. Unlike static data, the time series data vary with time. The...
The development of the 6th channel of China Central Television (cctv-6, movie channel) brings a new way to view a movie and a better experience to user. Compared to the theaters, cctv-6 has the advantages of viewing conveniently and it spends little. But from in terms of visual impact and sound, watching movies on TV is not as good as go to the cinema, and the time also to have certain hysteresis...
With the growing market of IP movies, it's becoming more and more important to know their box office features. This paper studies the influence degrees on the film box office from IP features, including the IP type, rating score and following number, combined with other factors that affect the film box office. By using the 2015 data of the films shown in China, the empirical analysis is conducted...
This paper introduced the personalized recommendation technology to the news system. Especially, in order to meet the demand of the users' personality and ease the problem of data sparse, the research work proposed the hybrid collaborative filtering algorithm based on news recommendation. By improving correlation coefficient formula via adding news hot parameter when calculating the similarity of...
This paper proposed an impact analysis framework of three-dimensional indoor location technology based on RSSI. The impact analysis model is set to compare the location precision under different types of noise. The result illustrate that the designed impact analysis tool achieves the perfect three-dimensional indoor location results combined cost, location accuracy with filter. To reduce the impact...
This paper proposed a newly designed model to satisfy the growing demand of three-dimensional indoor positioning services. The existing different positioning services cannot meet the demand in our daily life, and the increasingly need of three-dimensional positioning services has attracted more attention. In this paper, we implement our analysis and research via studying the evaluation results of...
This paper proposed a solution to the three-dimensional indoor positioning system based on location fingerprint method. Especially, it proposed an automatic offl-ine collection method and realizes an off-line collection system, including a collection server and a mobile collection application, which can be used to collection the information of location fingerprint conveniently, and also improve the...
This paper introduced the personalized recommendation technology to the news system. In order to meet the demand of the users' personality and ease the problem of data sparse, the research work proposed the hybrid collaborative filtering algorithm based on news recommendation. It improved correlation coefficient formula by adding news hot parameter when calculating the similarity of users, and then...
This paper proposed a solution using multi-index method to evaluating node importance locally and globally with adjusted parameters in the network by considering several evaluation indexes. Especially, it adheres to the principles that the node importance of the network is relevant to the value of the node itself together with the centrality of neighbor nodes from a local point of view. Moreover,...
This paper proposed a solution to three-dimensional indoor positioning system based on location fingerprint method. Especially, this paper proposed a automatic offline collection method and realizes a off-line collection system, including a collection server and a mobile collection applications, which can be used to collection the information of location fingerprint conveniently, and also improve...
In this paper, the characteristics of five main subjective weighting methods for multi-attribute decision making problems are analyzed by means of horizontal and longitudinal comparison. And the five weighting methods are applied to the field of broadcasting and television program evaluation respectively to obtain the evaluation results calculated by five methods and analyze the relationship between...
The film industry is one of the fastest growing industries in the world, especially the growth of Chinese movie box office. Now the film industry is still a high risk industry. As a capital intensive industry, movie box office forecasting is particularly important to investment and financing of the film, cinema advertising sales, row of film theaters, marketing, operations and various aspects. Based...
With the increasing of comment data about television programs on Internet, using text mining technology to analyze the massive data so that it can make subjective evaluation about television programs. It can give decision support and improve competitiveness for each department in radio and TV industry. This paper mainly introduces how to using sentiment-oriented pointwise mutual information (SO-PMI)...
Aiming to meet the demand of intellectual delivery business, this paper puts forward Broadcast Television user dividing groups technology based on concept data clustering ensemble. Firstly, by Glass data, Blance data and Zoo data in UCI, the paper verify that concept data clustering ensemble technique based on K-MODES method can get more stable and more accurate results than K-MEANS method. Secondly,...
This paper constructs a model for the forecasting of movie box office revenues during the pre-production period. Our study selects some basic variables in film forecasting field such as production country, genre, seasonality and star value. This paper divides the box office revenue into seven intervals ranged from 'flop' to 'blockbuster' and chooses Classification and Regression Tree to predict box...
With the improvement of web 2.0 technology and rapid increasing number of internet users, the massive user generated content (UGC) becomes an important factor influencing product image and users' decision. Combined with data mining technology, it can effectively avoid the limitation of traditional satisfaction survey method, and it can improve effectiveness and accuracy. The paper puts forward an...
With the development of digital cable interactive business and the diversification of the customers' demand, grouping TV programmes based on preferences of users effectively is vital for market segmentation and differentiation. The study summarizes the main principle and characteristic of clustering algorithm, and uses K-Means algorithm to show TV programmes preference grouping based on 52392 subscribers...
With the popularity of smart phones and the rapid development of wireless communication technologies, people tend to determine their indoor location in the complex indoor environment such as a library, exhibition hall and shopping malls using location-based services. However, in many scenarios, two-dimensional positioning system has been unable to meet the needs. In this paper, we research on the...
With the constant increasing of the TV programs text evaluate data on Internet, using emotional tendentiousness analysis technology to analyze the mass text data will help the program producers, program promulgator, network operator and government department improve their competitiveness and make a better decision, accordingly improve the competitiveness of the broadcasting and TV. The paper is on...
This paper studied the information overload brought by abundant digital television (TV) program resources and media image which need to adapt to the changing market environment by crowd mining based on audience behavior analysis. When the audience crowd is classified to several levels, personalized audience behavior analysis method and group audience behavior analysis method are proposed separately...
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