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PRODUCTJUN 18, 2020


HOW TIKTOK RECOMMENDS VIDEOS #FORYOU

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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.

Note: At the TikTok Transparency Center in Los Angeles, invited experts will
have the opportunity to learn how our algorithm operates along with reviewing
TikTok source code, which will be made available at the center for testing and
evaluation.


PRODUCTJUN 18, 2020

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