A Study on Distribution of Profits from Additional Rights in Movie Content

2015 ◽  
Vol 2 (1) ◽  
pp. 28
Author(s):  
Sekyoung Shin
Keyword(s):  
Movie Content

Movie Content Analysis, Indexing and Skimming Via Multimodal Information

Video Mining ◽  
2003 ◽  
pp. 123-154 ◽  
Author(s):  
Ying Li ◽  
Shrikanth Narayanan ◽  
C.-C. Jay Kuo
Keyword(s):  
Content Analysis ◽  
Movie Content ◽  
Multimodal Information

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

scholarly journals An inside look into the complexity of box-office revenue prediction in China

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668484 ◽  
Author(s):  
Jia Xiao ◽  
Xin Li ◽  
Shanzhi Chen ◽  
Xuhui Zhao ◽  
Meng Xu
Keyword(s):  
Correlation Coefficients ◽  
Online Behavior ◽  
Online Data ◽  
Box Office ◽  
Movie Content ◽  
Stepwise Method ◽  
Analysis System ◽  
Internal And External Factors ◽  
Internet Of Things Technology ◽  
The Internet Of Things

In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.

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