Tiktok Videos

TikTok Video recommends videos

TikTok Videos 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 Videos 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 Videos 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 Videos 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 Videos.

Tiktok Videos
Tiktok Videos

The basics tiktok Videos 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 Videos, 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.

Tiktok Videos
Tiktok Videos

Curating your personalized For You feed

Getting started

How can you possibly know what you like on Tiktok Videos 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.

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