A Validation of the Algorithmic Formula for the Relevance of Douyin Users’ Favorite tags
DOI:
https://doi.org/10.54691/bcpbm.v28i.2222Keywords:
Neural Network, Prediction Model, Big Data.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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