YouTube's recommended algorithm is unknown and complex. However, it dictates a majority of viewing content for billions of users, which greatly affects the media we consume.
What it does
Our project gathers data about YouTube videos:
- Date Published
- Recommended Videos
Then, it calculates a weight for each video, based on YouTube's computed category and views. A node graph is then created, using the weights as distance between nodes.
How we built it
Challenges we ran into
Due to the data set being extremely large, we came across the issue of computing times being too long. Also, we had little to no prior experience with most of the tools used to create our project.
Accomplishments that we're proud of
This weekend, we were capable of successfully collaborating to get a lot of work done in a short amount of time. In addition, we developed skills with and learned a lot about tools that were new to us.
What we learned
What's next for Visual Representation of YouTube Recommended Algorithm
In the future, we would like to expand this project to include more criteria, and test the criteria to find what is most correlated with the YouTube algorithm. Also, we plan to scale up our processing power, to increase the number of data points we can collect and process.