| dc.description.abstract | With the increasing maturity of Internet technology and the development of entertainment culture, people are increasingly relying on watching movies and dramas through digital video platforms. In the era of information explosion, the amount of information has risen sharply, and the information on the video and audio platforms has become increasingly abundant. How to find movies that people are interested in watching in a vast array of materials has become a topic that needs to be addressed. As one of the major daily spiritual entertainment projects, movies also have the problem of information overload.
In order to achieve the purpose of personalized recommendation, this study uses user data, platform basic data, and user′s viewing behavior on the online video platform, and uses data mining technology to analyze users in groups so that users in the same cluster have The most similar movie viewing interests allow the users of different clusters to have the most disparate movie viewing preferences and recommend to the user that the user of the same cluster has never watched the top 10 movies when he enters the platform.
Applying the shopping basket analysis concept in association analysis to the movie watched by the cluster user, the same user viewing the movie every two months as a shopping basket transaction to analyze what movies the same group of users usually see, and which movies are often consecutive Watch and watch those videos after watching them.
This study also analyzed the behavior of users watching movies in January-February 2018 through association rules. The majority of users who found that audio and video on the F line like to watch horror films published in the United States in 2011.
Keywords: online video platform recommendation system, data exploration, cluster analysis, association analysis | en_US |