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TikTok explains how the For You feed recommends videos
TikTok's own account of its For You feed lists three groups of signals and says strong signals count for more than weak ones. It is a company description, not an audit, and it gives no weights.
- Where it sits
- Consumer applicationUse of psychology, or claims about it, in commerce, policy or product design.
- Evidence level
- Company self-reportThe main evidence is the company’s own account. It has not been independently audited.
- Organizations
- TikTokAn organization is listed where the sources concern it. The text says exactly how.
- Sources inspected
- 2 of 22 with the relevant text, company document or archive item checked, 0 as an abstract or reference article, 0 not verified.
What happened
On 18 June 2020 TikTok published a newsroom post titled How TikTok recommends videos #ForYou. It describes how the For You feed picks videos and groups the inputs into user interactions, video information, and device and account settings. A page on TikTok's transparency site adds that the system predicts how likely a user is to like, share, comment on or skip a video and combines the predictions into an overall score for each video.
The psychological idea
TikTok describes a ranking system in which signals carry different weights. It says strong signals, such as finishing a longer video, count for more than weak ones, and that device and account settings count for less because users do not actively choose them as preferences. It says follower count and past high-performing videos are not direct factors. It also says the feed generally will not show two videos in a row with the same sound or by the same creator.
In practice
The post gives examples of each group: videos liked or shared, accounts followed, comments and content created; captions, sounds and hashtags; language, country and device type. The transparency page describes user controls named Not interested, keyword filters and a For You refresh. Neither page says how much each signal or prediction counts. The post also mentions a transparency center in Los Angeles where invited experts can learn how the algorithm works, with source code to be made available there for testing and evaluation.
Why it mattered
It is TikTok's own published account of the For You feed, and a company description of this kind gives outside testers something to compare with their results. The post states that finishing a longer video is a strong signal, which is a point that independent experiments can check.
Limits and qualifications
This is company-self-report, not an audit. The pages read give no weights, model details or full list of signals. The offer of expert access to source code is a company statement; no report of who reviewed what was opened for this record. A list of signals does not show what effect the feed has on viewers or what content any person sees.
Evidence behind the claims
On 18 June 2020 TikTok's newsroom listed three groups of recommendation inputs: user interactions, video information, and device and account settings.
TikTok says strong signals such as finishing a longer video get more weight than weak ones, and device and account settings get lower weight because users do not actively express them as preferences.
TikTok says neither follower count nor previous high-performing videos are direct factors.
TikTok says the feed generally will not show two videos in a row made with the same sound or by the same creator.
TikTok's newsroom post says invited experts can learn how the algorithm works at its Transparency Center in Los Angeles, and that source code will be made available there for testing and evaluation.
TikTok's transparency page says the system predicts how likely a user is to like, share, comment on or skip a video and combines the predictions into an overall score for each video; it does not disclose the weights.
TikTok's transparency page lists user controls: Not interested, keyword filters for the For You feed, and a For You refresh.
People involved
In the Psy101 course
- OpenStax Psychology 2e, section 2.1: Why Is Research Important?Chapter 2, Psychological Research. Context. The section discusses this topic.Opened the page: it urges asking about a claimant's expertise and what they might gain, which applies to a company describing its own system.
This is course relevance only. It is not evidence for the historical or corporate claims on this page.See the full course map.
Sources and inspection
2 of 2 sources were opened and read at the level shown. A source marked as not verified supports no claim on this page by itself.
- [1]How TikTok recommends videos #ForYouTikTok Newsroom, 2020. Company document. Company document.Company document readReviewer re-opened the TikTok Newsroom post through the fetch tool and read the date, the three signal groups, the weighting passage, the follower-count passage, the diversity passage and the Transparency Center passage as returned by the tool.
- [2]Recommendation systemTikTok Transparency Center, Undated. Company document. Company document.Company document readReviewer opened the transparency page: prediction and overall-score passage, no weights disclosed, and user controls. No publication date was shown. A footer year was previously used as a placeholder; it is now null and is not treated as a publication date.
Access note: Both sources are TikTok's own pages. No independent review of the source code or of the weighting was found or opened.
Independent source review: Claude review agent, independent of the research agent, 2026-10-21. Verdict: pass-with-edits.