Evaluate the a priori rating of the content

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same time, they can obtain more economic benefits through content publishing. Then, the recommendation system can do the following things for the author at this stage: Guaranteed traffic requirements: Different content publishing traffic guarantee models can be designed for content authors with different fan levels and different verticals to ensure that the content can obtain a certain traffic base when distributed, and can be based on the quality of the author's previous content. , and provide a corresponding traffic model based on factors s

uch as the a priori rating and the author to achieve the basic Afghanistan WhatsApp Number traffic needs of the author's published content. Climbing traffic demands: Based on the posterior data of the content, the content can be distributed to users in the verticalfield, and the effect of traffic distribution can be achieved by identifying high-quality content. Create popular products for waist authors: In addition, in the process of waist authors moving towards head authors, there is one link that cannot be avoided, and that is "hot products". After the a priori data is determined, it is also very important to





conduct posterior testing on the content and design different traffic ladder models based on the posterior ranking of the content, so as to try to mine the content of waist authors and create a hit. Author cycle multi-target distribution: In addition, after different content is distributed, its corresponding positive feedback will be different. The recommendation system can integrate the content's fans, click-through rate, collection rate, dwell time and other goals to evaluate the user's current location. Comprehensive evaluation and distribution are carried out in different