# Automatic Movie Tag Generation System for Improving the Recommendation System
## 1 Introduction  
With the development of the Internet, a large amount of data and content are being produced worldwide. Today, data have become a key factor in everything, the size of data is growing exponentially, and the penetration rate of e-commerce in web--based businesses varies across business areas, such as the US (266 million, 84%) and France (fifty-four million, 81%), but, every month, there is a steady increase in new members. According to IBM, \"Every day, Internet users generate 2500 trillion bytes of information, which is 90 percent of today's information on the planet in the last three to four years\" [1].  
Many studies are being conducted for such data and content consumption, and they continue to develop. Among them, a recommendation system is a technology that has been used to provide data and content to users on the Internet from the past.  
A recommendation system is an information tool that helps users to determine the items they want from a large number of items available. The main goals of a recommendation system are to predict the ratings given to items by specific users and to help users find the best solution in a list of available items [2, 3]. Many companies, such as Netflix, YouTube, and Amazon, use recommendation systems to serve users and make profits [4].  
For example, if we want to buy books, listen to music, watch movies, etc., there is one recommendation system that is working in the background that makes suggestions to the user based on his previous actions [5]. Many platforms--such as Netflix, which suggests movies, Amazon, which suggests products, Spotify, which suggests music, LinkedIn, which is used for recommending jobs, or any social networking sites that suggest users--all work on a recommendation system [6, 7, 8].  
These companies benefit from recommendation systems and are shown in Table 1.  
As such, with the continuous development of research and technology, the recommendation system method continues to develop, and research is being conducted more actively based on content-providing platform companies.  
A movie recommendation system helps movie lovers to avoid looking into vast databases online and also reduces the time needed to search for the right movie by suggesting top--tier movies.  
These movie recommendation systems use various techniques, such as content-based filtering algorithms and collaborative filtering algorithms. Among them, a content-based algorithm generates and utilizes various keywords, such as genre, title, director, actor, and atmosphere, of the movie in a way that recommends using the movie's metadata or tags together with user information. Currently, the movie's metadata or tags are manually entered by the operator, which means that the recommendation system has not been automated overall.  
In this paper, we conduct a study to automatically generate tags (genre, mood, producer, title) of movie content required for recommendation systems through artificial intelligence and to verify the consistency of tags derived by the proposed method and manually entered tags.  
This paper is structured as follows. Section 2 introduces the theoretical background related to speech processing. Section 3 includes related studies on automatic tag generation for movie content. In Sections 4 and 5, the proposed method and results are described. Finally, Section 6 presents the overall conclusion of the proposed method.