A Validation of the Algorithmic Formula for the Relevance of Douyin Users’ Favorite tags
Bibliographic record
Abstract
By June 2022, the number of monthly active users of Douyin has exceeded 800million. Since its launch, it has been favored by many users because of its tag push mechanism. The tag push mechanism has become one of the core characteristics. This paper investigates the accuracy and feasibility of a recommendation formula for the Douyin tags’ relevance algorithm. A questionnaire survey is carried out and then empirical analysis in terms of the collected data is carried out to find out the relevant coefficients between different tags and compare it with the actual data collected by Douyin. Based on the results of the questionnaire, the food and life tags had the highest relevant coefficients. Subsequently, the food tag is selected as the main object of study. After further processing and analysis, the relevant coefficients between food and life tags was even higher, confirming the feasibility of the tag relevance recommendation algorithm. These results shed light on guiding further exploration of Douyin algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".