Hello. Welcome to 'This Is My Architecture'.
My name is Andy Kim, Solutions Architect at Amazon Web Services.
Today, I invited Seunghwan Park from Samsung Electronics to explain their architecture.
- Hi. - Hello.
I am Seunghwan Park, who is developing the Game Launcher service at Samsung Electronics.
I also really enjoy playing games.
What is the Game Launcher service?
The Game Launcher service collects and displays games installed on Samsung Galaxy smartphones.
We strive to provide users with a better game environment.
Through the user's game history, we provide a ranking for each game
by calculating statistics on the user's game play time or individual play time.
It also recommends video content related to the game.
In the case of recommending the video content mentioned at the end, it will not be easy to select the right content.
How are you selecting video content on the game launcher right now?
Yes, as I mentioned earlier, in our game launcher, we're collecting the user's game history.
Rankings are made based on this record, and we are collecting videos for the games that are in high rankings.
I know you have introduced artificial intelligence. Why did you introduce it?
If the previous game-related videos were simply searched for by the game title,
After moving to 4.0, the game-related videos are collected first,
and then after grasping how relevant the game is, we recommend the most highly relevant videos.
Artificial intelligence seems quite difficult. What was the most difficult part of introducing it?
Yes, first, we needed to be able to confirm the game-related video,
but, in the past, we lacked experience in machine learning. We also had a very short development period.
So, we started using Amazon Comprehend which is a fully managed service.
You said you have introduced a service called Amazon Comprehend. How did you apply this service?
and what were the advantages?
Yes, first, since it is a fully managed service, the development period has been significantly shortened.
And, it was different during the time when a video was recommended simply by searching by the game title.
Now it's possible to provide a video that is more relevant to the game.
Yes, It looks like you used the Custom Classification which is one of the features in Comprehend.
We can see the architecture diagram of the game launcher service.
Could you explain a little about this diagram?
Yes, first, there are two main categories.
The lower part shows that it creates the most basic model for artificial intelligence and machine learning,
and the upper part shows how we collect video related to the game
and determine how relevant the collected video is.
Let me explain the part of creating a model. Basically, a module called a Crawler collects
game-related text from the web.
After that, it will be saved to S3.
Then, Crawler asks Comprehend to create the model.
When asked to create this model, it provides the training data in S3 as input to Comprehend.
In a few hours, Comprehend can generate the model and then gather results for it.
After that, in the stage of analyzing the collected videos, Lambda periodically calls a module called Video Analyzer.
Video Analyzer asks Comprehend to analyze the video titles or texts
from the videos we have collected.
Then, the analyzed result will be provided from Comprehend.
We can see which games and how many scores are matched and get the results from Comprehend.
The result is stored in Aurora DB.
And in this Aurora DB, the result of which game and match resulted in a certain score is stored.
The result is analyzed through EMR and then stored in DynamoDB.
When saving in DynamoDB, only video content that exceeds a certain score is saved.
Then you can see which video should be recommended in the game launcher application.
Thank you for explaining the architecture and introduction of various AI technologies for today's Game Launcher.
This has been 'This Is My Architecture'.
Thank you.