Today's Internet self-media world, in the final analysis, is still an era of traffic, an era where traffic is king. Whether you are on Xiaohongshu or other self-media platforms, you need to know the traffic rules of the platform. Today, I would like to share with you what the traffic rules of Xiaohongshu are. Let us analyze the mechanism and algorithm of Xiaohongshu through traffic rules.
1. What are the traffic rules of Xiaohongshu?
If we analyze it from the underlying logic, the platform traffic rules are actually very simple. Grasping two key core points, everything is clear at a glance. These two key core points are content matching tags and social relationship chain recommendations.
How to understand it? For example, if you usually like to read articles about weight loss, the platform will recommend more notes about weight loss to you. After the notes are pushed to fans, based on fans’ likes, collections, comments, reposts, and concerns, internal scores will be assigned to the notes to determine Do you want to continue pushing to other fans. This scoring system is called CES (community engagement score) inside Xiaohongshu.
Notes with high scores will also be supported by traffic from Xiaohongshu and Baidu search. This type of traffic is very persistent, and your notes can be posted for several years and still maintain good likes, comments, favorites, etc.
Of course, only after your notes are included in the system will they be recommended and get more reading.
From this, we can infer the recommendation mechanism of Xiaohongshu, which is to continuously push it to relevant users. Then it will be divided into two modes because of the different software habits and behaviors of users:
1. Machine algorithm recommendation: recommend relevant content to users according to their preferences (search keywords), in order to allow users to collect more.
2. Extended reading recommendation: Recommend content that users are interested in (related content that users browse), allowing users to extend their reading, and increase users' likes, favorites, reposts, etc.
This kind of recommendation is actually a post-training recommendation, that is, the platform doesn’t know what you like at the beginning, so it makes accurate pushes after collecting your data.
There is another recommendation mechanism that will exist and develop continuously from the beginning, that is, algorithm recommendation based on raw data:
1. Recommendation based on friend relationship: The content of the account that the user actively follows is presented here as a recommendation to the user in a form similar to the information flow of the circle of friends.
2. Nearby recommendation based on distance: It will recommend notes within 20km to the user.
3. Editor's recommendation: The platform has its own official account, which will collect high-quality notes written by users and then recommend them.
The advantage of this is that as long as you output valuable content and publish high-quality notes, you can also get traffic recommendations from the platform without fans. In this process, more fans will pay attention.
The above is the introduction of what the flow rules of Xiaohongshu are. I hope you will gain something~