Spaces Recommendations
The Spaces Tab is a dedicated part of the 𝕏 app that shows a list of live Spaces that a user can scroll and select from. The goal of the Spaces Tab recommender is to personalize the list of Spaces for a user so they can find ones that they want to listen to.
How does 𝕏 find Spaces
When the user loads the Spaces Tab, the recommender uses metadata about the currently live Spaces and information about a user’s past listening selections as inputs to a machine learning model that’s trained to rank Spaces in order of the likelihood that a user will choose to listen.
How does 𝕏 decide which Spaces to show you
𝕏 uses machine learning algorithms to influence how we rank the Spaces we present to you. Space creators tag Spaces according to Topics. This makes it possible for us to track how long a user listens to Spaces related to a specific Topic. We combine this information with other features, such as the number of participants, language, follow relationship, the popularity of the host, and so forth to develop an overall prediction.
How you can influence the Spaces you see
You can influence the Spaces that are shown across 𝕏 by reporting a Space if you think it violates our rules, and you can influence Spaces that are shown to you by reporting that you are not interested in a Space. You can also influence the Spaces shown to you by changing your language preference, or the Topics you follow. (Learn more, here.)
𝕏 has also designed tools that help you control all content that you see across the platform and to protect you from content you consider harmful. (Learn more, here.)
More information
For a more detailed view of how our Spaces recommendation system works, please see:
An overview from our engineering team below; or
Our About Spaces help center article, here.
System Overview
Our recommendation system for Spaces is trained on information we collect from a variety of sources. The diagram below describes the primary storage systems involved, when data is written to them, and how they relate to each other:
As illustrated above, when a user opens the Spaces tab, their client makes a request to the Proxsee service for a list of available Spaces. Proxsee is the main backend service for Spaces and is responsible for major user requests such as login, following and watching; it calls other micro-services to power different user activities. Proxee receives metadata about each Space, including any Topics that the host has tagged it with. This makes it possible for us to track the amount of time a user spends listening to different Topics. We also rely on two tables: SpacesMLRequesterFeatures, which contains information about the user, such as their preferred language and historical watch data, and SpacesMLCandidateFeatures, which contains information about available Spaces, such as their titles, any tagged Topics, the number of active watchers, and so on.
In addition to BroadcastViewEndedDay, which contains a user’s watch duration of a certain Space, where they view it from, and so on, our models use additional datasets that contain serving time information. In addition, our models receive values for the total past watch time for Spaces labeled as related to particular Topics. We use RedisLabs as the user feature store, which contains user’s historical watch data. For each space, the host may tag it with specific Topics such as Cryptocurrencies, Business & Finance, Music, Gaming, Sports, and so on. When the user stops watching a Space, we collect the watch duration of the tags and Topics associated with this space, and aggregate them to the user’s historical watch data.
The specific inputs our models use to recommend Spaces include the following. You can think of the model as a mathematical function that takes these values as arguments and outputs a probability that the user will listen to the Space. Internally, the model is a neural net trained on historical data to estimate the probability given a set of inputs. Examples of this include:
title
The title of the space
is_verified_user
The Space host is a verified user
locale
The user’s language
broadcast_language
Space language
is_trending
A weighted sum of the number of listeners, number of followers of the hosts and speakers of the spaces
is_featured
Whether the Space has been marked as “featured” by a human curator
duration
How long has the broadcast been running
n_watchers
Number of watchers on app
n_stickers
Number of watchers that watch for more than 15s
n_entrants
Number of listeners who have entered (and possibly already left)
n_watchers_web
Number of watchers on web
n_hearts
Number of emoji reactions
n_hearts_capped
A capped number of emoji reactions
n_comments
Number of posts shared in space
n_comments_capped
Related to number of posts shared in space
n_guests_followed
Number of speakers that the requester follows
n_admins_followed
Number of hosts that the requester follows
n_participants_followed
Number of listeners that the requester follows
session_count
Number of Spaces engagement session by user in past
n_overlapped_topics
Number of overlapped topics between current Space and previous Spaces user has engaged with in past N days
n_overlapped_categories
Number of overlapped categories between current Space and previous Spaces user has engaged with in past N days
avg_overlapped_topics_watch_time
Average watch time for overlapped topics between current Space and previous Spaces user has engaged with in past N days
avg_overlapped_categories_watch_time
Average watch time for overlapped categories between current Space and previous Spaces user has engaged with in past N days
n_uninterested
Number of users that clicked uninterested on Space
audio_space_type
Live or Recorded space types