wowdevqa posted: " #deeplearning.AI: What Makes TikTok Tick A leaked document gave reporters a glimpse of what makes TikTok's renowned recommender algorithm so effective. What's new: An internal report produced by TikTok's Beijing-based engineering tea"
A leaked document gave reporters a glimpse of what makes TikTok's renowned recommender algorithm so effective.
What's new: An internal report produced by TikTok's Beijing-based engineering team for nontechnical colleagues describes the short-form video streaming platform's formula for recommending videos to particular users, according to The New York Times. The Times received the document from an employee who was disturbed by TikTok's distribution of content that could encourage self-harm. The company confirmed its authenticity.
How it works: The company's primary goal is to add daily active users. A flowchart (see above) indicates that the primary factors that determine daily active use are time spent with the app and repeated uses ("retention"), which in turn are driven largely by interactions such as likes and comments and video quality as determined by the creator's rate of uploads and ability to make money from them. To that end, the recommender scores each video with respect to a given user and offers those with the highest scores.
The ranking algorithm applies a formula that, in simplified form, goes like this: Plike x Vlike + Pcomment x Vcomment + Eplaytime x Vplaytime + Pplay x Vplay. The Times report didn't define its terms.
A machine learning model predicts whether a given user will like a given video, comment on it, spend a particular amount of time watching it, or play it at all. This model apparently supplies the variables marked P (for predicted) and E (for estimated). Those marked V could be the value of that activity; that is, how much the company values a given user liking, commenting, watching for a certain amount of time, or watching at all. Thus the formula appears to compute an estimated value of showing the video to the user.
The document suggests various ways in which TikTok can refine the recommendations. For instance, it might boost the rank of videos by producers whose works a user watched previously, on the theory that they're more likely to engage with that producer's output.
Conversely, in a bid to avoid boredom, it might penalize videos in categories that the user watched earlier the same day. It also penalizes videos that aim to achieve a high score by asking viewers explicitly to like them.
The document suggests that Douyin, Tiktok's Chinese equivalent, relies on a similar recommender.
What they're saying: "There seems to be some perception (by the media? or the public?) that they've cracked some magic code for recommendation, but most of what I've seen seems pretty normal." — Julian McAuley, professor of computer science, University of California San Diego, quoted by The New York Times.
Behind the news: In July, The Wall Street Journal attempted to understand TikTok's recommender by creating over 100 automated accounts, each with a fake date of birth, IP address, and interests such as yoga, forestry, or extreme sports. TikTok homed in on most of the bots' interests in less than two hours. By analyzing the videos recommended to each account, the reporters determined that the algorithm gave the heaviest weights to time spent watching a video, number of repeat viewings, and whether the video was paused during playback.
Why it matters: TikTok has amassed over 1 billion monthly users since its founding in late 2016, and its recommender is an important part of the reason why. The secret sauce is clearly of interest to competitors and researchers, but as we learn more about social media's worrisome social impacts — such as spreading misinformation, inciting violence, and degrading mental health — it becomes vital to understand the forces at play so we can minimize harms and maximize benefits.
We're thinking: Compared to platforms that deliver longer videos, TikTok's short format enables it to show more clips per hour of engagement and thus to acquire more data about what a user does and doesn't like. This makes it easier to customize a habit-forming feed for each audience member.
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