How the Twitch “Recommended Streamer” Algorithm Works

If you’re a Twitch streamer looking to grow your channel, understanding the Twitch Recommended Algorithm can give you a huge edge. While Twitch hasn’t disclosed the exact details of its algorithm, we can infer some key aspects based on other recommendation models.

Here I’ll explain each of the algorithm’s methods, and offer some ways for you to take advantage and grow your Twitch channel significantly faster

1. Collaborative Filtering

Collaborative filtering is a common technique used in recommendation models. It helps viewers discover streamers with similar content or audience. Two types of collaborative filtering are:

User-based: Twitch identifies viewers with similar watching habits and recommends streamers they’ve watched.
Item-based: Twitch identifies similar streamers based on overlapping viewership and recommends them to users.

Collaborative Filtering Twitch Example Image — *Recommendations are based off of similar viewer preferences*

Tips for streamers:

Research other streamers that your dedicated viewers watch and learn from their content.
Collaborate with streamers who share a similar target audience to increase exposure.

The YouTube algorithm is similar in this regard. If you’re interested in knowing whether it’s worth uploading past streams or VODs to YouTube (spoiler alert: it totally is), check out this article on why you should and how it will lead to a gain in stream viewership

2. Content-Based Filtering

Content-based filtering uses attributes of the stream, like the game being played or the tags used, to match potential viewers with relevant content.

This is different from collaborative filtering, in that this model is only looking at the content you already like, versus looking at what similar users to you also like

Content Based Filtering for Twitch Streaming Algorithm Image — *Based on only what the singular viewer likes; not any similar viewers*

Tips for streamers:

Focus on a niche to attract a specific audience (such as speed runs, or challenge content).
Try to understand what exactly it is your viewers like about your stream, is it the game/content/stream vibe?
Use relevant tags and stream titles to help Twitch understand your content better.

3. Context-Aware Recommendations

Twitch considers contextual information, such as the time of day or day of the week, when recommending channels.

Moon Knight GIFfrom Moon GIFs

Tips for streamers:

Set a consistent schedule so the Twitch algorithm knows the best times to recommend your stream.
Understand what viewers may be looking for in a certain time slot (Ex: late night viewers, may prefer a more relaxed streamer)
Experiment with streaming at popular times to maximize exposure.

4. Reinforcement Learning

Reinforcement learning means Twitch learns from user engagement and adjusts recommendations to facilitate more interaction.

The more Twitch is able to learn from your channel’s chat, the more it will recommend you to other users (so more chatting = more recommending of your channel to potential new viewers)

Twitch Chat Example for Algorithm Image — *Lots of chatting makes the algorithm happy!*

Tips for streamers:

Encourage viewers to chat, even if it’s just emote spamming when the conversation slows down.
Engage with your audience to boost chat activity and viewer counts.

While some of this information is based on conjecture, most recommendation models, including Twitch’s algorithm, use these fundamentals. By understanding these components and implementing the tips provided, you can improve your chances of being recommended to potential viewers and grow your Twitch channel

If you’re trying to find the best game to stream as a smaller streamer, check out this guide!