TikTok provided the following guide to how it presents a stream of videos curated to users’ personal interests:
TikTok’s mission is to inspire
creativity and bring joy. We’re building a global community where you
can create and share authentically, discover the world, and connect with
others. The For You feed is part of what enables that connection and
discovery. It’s central to the TikTok experience and where most of our
users spend their time.
When
you open TikTok and land in your For You feed, you’re presented with a
stream of videos curated to your interests, making it easy to find
content and creators you love. This feed is powered by a recommendation
system that delivers content to each user that is likely to be of
interest to that particular user. Part of the magic of TikTok is that
there’s no one For You feed – while different people may come upon some
of the same standout videos, each person’s feed is unique and tailored
to that specific individual.
The
For You feed is one of the defining features of the TikTok platform,
but we know there are questions about how recommendations are delivered
to your feed. In this post we’ll explain the recommendation system
behind the For You feed, discuss how we work to counter some of the
issues that all recommendation services can grapple with, and share tips
for how you can personalize your discovery experience on TikTok.
The basics about recommendation systems
Recommendation
systems are all around us. They power many of the services we use and
love every day. From shopping to streaming to search engines,
recommendation systems are designed to help people have a more
personalized experience.
In
general, these systems suggest content after taking into account user
preferences as expressed through interactions with the app, like posting
a comment or following an account. These signals help the
recommendation system gauge the content you like as well as the content
you’d prefer to skip.
What factors contribute to For You?
On
TikTok, the For You feed reflects preferences unique to each user. The
system recommends content by ranking videos based on a combination of
factors – starting from interests you express as a new user and
adjusting for things you indicate you’re not interested in, too – to form your personalized For You feed.
Recommendations are based on a number of factors, including things like:
- User interactions such as the videos you like or share, accounts you follow, comments you post, and content you create.
- Video information, which might include details like captions, sounds, and hashtags.
- Device and account settings
like your language preference, country setting, and device type. These
factors are included to make sure the system is optimized for
performance, but they receive lower weight in the recommendation system
relative to other data points we measure since users don’t actively
express these as preferences.
All
these factors are processed by our recommendation system and weighted
based on their value to a user. A strong indicator of interest, such as
whether a user finishes watching a longer video from beginning to end,
would receive greater weight than a weak indicator, such as whether the
video’s viewer and creator are both in the same country. Videos are then
ranked to determine the likelihood of a user’s interest in a piece of
content, and delivered to each unique For You feed.
While
a video is likely to receive more views if posted by an account that
has more followers, by virtue of that account having built up a larger
follower base, neither follower count nor whether the account has had
previous high-performing videos are direct factors in the recommendation
system.
Curating your personalized For You feed
Getting started
How
can you possibly know what you like on TikTok when you’ve only just
started on the app? To help kick things off we invite new users to
select categories of interest, like pets or travel, to help tailor
recommendations to their preferences. This allows the app to develop an
initial feed, and it will start to polish recommendations based on your
interactions with an early set of videos.
For
users who don’t select categories, we start by offering you a
generalized feed of popular videos to get the ball rolling. Your first
set of likes, comments, and replays will initiate an early round of
recommendations as the system begins to learn more about your content
tastes.
Finding more of what you’re interested in
Every
new interaction helps the system learn about your interests and suggest
content – so the best way to curate your For You feed is to simply use
and enjoy the app. Over time, your For You feed should increasingly be
able to surface recommendations that are relevant to your interests.
Your
For You feed isn’t only shaped by your engagement through the feed
itself. When you decide to follow new accounts, for example, that action
will help refine your recommendations too, as will exploring hashtags,
sounds, effects, and trending topics on the Discover tab. All of these
are ways to tailor your experience and invite new categories of content
into your feed.
Seeing less of what you’re not interested in
TikTok
is home to creators with many different interests and perspectives, and
sometimes you may come across a video that isn’t quite to your taste.
Just like you can long-press to add a video to your favorites, you can
simply long-press on a video and tap “Not Interested” to indicate that
you don’t care for a particular video. You can also choose to hide
videos from a given creator or made with a certain sound, or report a
video that seems out of line with our guidelines. All these actions
contribute to future recommendations in your For You feed.
Addressing the challenges of recommendation engines
One
of the inherent challenges with recommendation engines is that they can
inadvertently limit your experience – what is sometimes referred to as a
“filter bubble.” By optimizing for personalization and relevance, there
is a risk of presenting an increasingly homogenous stream of videos.
This is a concern we take seriously as we maintain our recommendation
system.
Interrupting repetitive patterns
To
keep your For You feed interesting and varied, our recommendation
system works to intersperse diverse types of content along with those
you already know you love. For example, your For You feed generally
won’t show two videos in a row made with the same sound or by the same
creator. We also don’t recommend duplicated content, content you’ve
already seen before, or any content that’s considered spam. However, you
might be recommended a video that’s been well received by other users
who share similar interests.
Diversifying recommendations
Diversity
is essential to maintaining a thriving global community, and it brings
the many corners of TikTok closer together. To that end, sometimes you
may come across a video in your feed that doesn’t appear to be relevant
to your expressed interests or have amassed a huge number of likes. This
is an important and intentional component of our approach to
recommendation: bringing a diversity of videos into your For You feed
gives you additional opportunities to stumble upon new content
categories, discover new creators, and experience new perspectives and
ideas as you scroll through your feed.
By
offering different videos from time to time, the system is also able to
get a better sense of what’s popular among a wider range of audiences
to help provide other TikTok users a great experience, too. Our goal is
to find balance between suggesting content that’s relevant to you while
also helping you find content and creators that encourage you to explore
experiences you might not otherwise see.
Safeguarding the viewing experience
Our
recommendation system is also designed with safety as a consideration.
Reviewed content found to depict things like graphic medical procedures
or legal consumption of regulated goods, for example – which may be
shocking if surfaced as a recommended video to a general audience that
hasn’t opted in to such content – may not be eligible for
recommendation. Similarly, videos that have just been uploaded or are
under review, and spam content such as videos seeking to artificially
increase traffic, also may be ineligible for recommendation into
anyone’s For You feed.
Improving For You
Developing
and maintaining TikTok’s recommendation system is a continuous process
as we work to refine accuracy, adjust models, and reassess the factors
and weights that contribute to recommendations based on feedback from
users, research, and data. We are committed to further research and
investment as we work to build in even more protections against the
engagement bias that can affect any recommendation system.
This
work spans many teams – including product, safety, and security – whose
work helps improve the relevance of the recommendation system and its
accuracy in suggesting content and categories you’re more likely to
enjoy.
Ultimately, your For
You feed is powered by your feedback: the system is designed to
continuously improve, correct, and learn from your own engagement with
the platform to produce personalized recommendations that we hope
inspire creativity and bring joy with every refresh of your For You
feed.
