Twitchly

Providing efficient automated solutions to Twitch users since 2018.

Overview

Twitchly is a project started by a team of enthusiastic engineers from UC Berkeley. Using Twitch API and Data Science techniques, we are looking to answer two primary questions:

• Given a channel, who should the channel partner/collaborate with to gain the most positive growth (defined in increase of viewership/following/subscription)?
• Given a user, which channels can we best recommend to the user?
• This project MVP is complete!

Resources

Much of the data we will be leveraging will come directly from Twitch API.

Follow this guide that I wrote up to get familiar with some basic API functionalities.

Tackling the Problem: V1

For this project, we want to first answer the question:

How similar are two channels?

Once we can answer this comparison question, we can answer our two primary questions stated in the Overview.

Let's talk about the second question which has a sub question of:

Should we recommend channel (c) to user (u)?

We can start this approach with a simple greedy algorithm:

• Find the set of channels that we could recommend to u by looking at all the channels u is currently following/subscribed to (u_channels), see which other users are also following those channels, and then for each of these users, finding who they follow.
  • The output should be a mapping of (key -> user : value -> followed_channels)
• We filter through the results by only selecting key-value pairs that have a certain % set match of followed_channels as u_channels. (Note we can optimize our filter by actually doing a pre-filter in step 1).
• For each value in the key-value pairs, we make a comparison to see how similar each channel is with the original u_channels and provide it a relevance score.
• We return the top 5 channels with the highest relevance score.