Tiktok to centralization

1. Decentralization is a phenomenon or structure, but the best explanation for this phenomenon is the concept of blockchain. If you want to have a thorough understanding, you can go to the community app of lianpai to have a look

2. According to Hou Jietai: the so-called centralization refers to subtracting the mean value of a variable from its expected value. For sample data, each observation value of a variable is subtracted from the sample average value of the variable, and the transformed variable is centralized
for your question, subtract the mean from each measurement.

3. Tiktok algorithm is actually a funnel mechanism, which is basically consistent with the principle of recommendation algorithm of the headline's de centralization. Tiktok tiktok is divided into three steps:

first, cold start flow cell exposure

suppose that 1 million people upload short videos every day on the shaking, and the jitter will randomly assign each cold video to a cold start flow pool with an average exposure. For example, after each short video is sent out by auditing, there are 1000 exposures

second on average. Data selection

tiktok will analyze 1000 points of the 1 million short videos, analyze the data of each dimension such as point, attention, comment, forwarding and so on, and then pick out videos with more than 10% indexes, and then distribute 100 thousand times on average. Then go to see which are like, follow, forward and comment more than 10%, and roll into the next round of larger flow pool for recommendation

thirdly, the boutique recommendation pool

through round after round of verification, short videos with extremely high liking rate, playback completion rate, comment interaction rate and other indicators are screened out to have the opportunity to enter the boutique recommendation pool. When users open the pool, they see videos with tens of millions of likes< br />
: " We are paladins. We can't let revenge occupy our consciousness& quot;,

4. Tiktok will detect whether there are plicate videos. If your video is released on the platform, the recommended amount will be very small or even several hundred or directly, so the original is very important.
repetitive video or highly similar video
is easy to be considered as
so we see many of the chattering tutorial to teach you how to do porters
we do not recommend to do these
we encourage you to do original
to be able to be autonomous. The value of the output content
in order to form their own fans effect
such fans can also become everyone's effective fans
and the other situation is that
the play goes up and up and stops
in fact, this is the problem that the comments on likes are too low
only good works with strong visibility can maintain a high rate of likes and ensure that the video is continuously pushed
short The rise of video platform
has made more people get the chance to participate in this video carnival.
shorter time further lowered the publisher's threshold
micro-blog tiktok wrote 140 words, and the hands became the
15 is also a major breakthrough in the new media instry.
the personality is vivid. Sharing
, making video content proction from specialization to grassroots
everyone can upload and share their content. Everyone can be heard and seen
want to know more buddies to see their heads.

5. The hardware is completely OK, and the graphics card 1060 can be fully configured. The processor i78750h is all right. It's smooth

6.

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extended data:

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7. After uploading the video, the machine recommends it to the people who may be interested in your video tags in a small range, that is, a small flow pool. It's about 20-250 people (including contacts friends, fans of accounts, fans of music accounts, fans who like or pay attention to this topic, fans in the same city and randomly allocated by the system)
calculate the number of comments, reviews and shares of the audience in unit time
the specific formula is: popularity = a number of comments + B number of likes + C number of shares, coefficient a, B, C will fine tune in real time according to the overall algorithm, roughly: C & gt; A> B This step is called the first recommendation. This is why we usually see the content in the recommendation, some of the interaction rate is almost 0. Because you're the first audience of this video.