I don’t understand the recommendation mechanism of the video account. How can I increase my followers and realize monetization?

For creators who want to make their video accounts successful, they must first understand the important traffic and recommendation algorithms of the platform itself.

Today, I will mainly talk about social recommendations and personalized algorithm recommendations in the WeChat video account distribution mechanism:

1. Social Recommendation

1. Social connections

The social relationship chain is very important in the recommendation logic of WeChat Video Account. For example, the content posted and liked by your friends will be recommended first. This can be seen from the three options displayed at the entrance of WeChat Video Account: "Follow, Friends♡, Popular".

2. Content Value

High-quality content is an eternal topic that any self-media platform adheres to. If a platform wants to gain users, it ultimately relies on high-quality content, so video accounts will also give priority to recommending high-quality content.

In fact, this rule is most obvious when there are more video accounts created by WeChat friends. When you open the video account, you will find that the videos you can see from your friends are all relatively good video content.

3. Like and interactive comments

The so-called "social recommendation" is inseparable from interactive behavior. The value and popularity of content are judged by users' likes, reposts, comments, etc., so as to enter a larger traffic pool and gain more exposure recommendations.

2. Personalized Recommendations

According to analysis, the current personalized recommendation algorithm of the video account adopts the method of "interest tags + positioning + hot spots + random recommendations".

1. Interest tags

The system will use a series of big data algorithms to infer what content the user may like based on the user's daily behavior, activity trajectory, interests, occupation, age and other labels.

2. Geolocation

People in the same city or living in a similar distance may have some overlap in interests due to their geographical location, such as "the same city, a certain tourist attraction", so geolocation is often an important part of personalized recommendations.

3. Hot Topics

Real-time hot online events and hot online topics, as public events, often attract the attention of a large number of users.

Then, based on the user's collaborative filtering (analyzing user interests, finding similar (interested) users to the specified user in the user group, and combining the evaluations of these similar users on a certain information to form a system prediction of the specified user's preference for the information), the recommendation system recommends high-quality content related to hot topics on the Internet to the user.

3. Traffic Distribution Rules

After a long period of analysis on the use of video accounts, we have summarized the recommendation mechanism of video accounts as follows:

1. After a creator publishes a video, it will first be recommended to friends who have followed the video account. If their friends are not interested, the exposure recommendation mechanism of the video account will not be triggered. The video will only receive one viewing traffic and will not enter a higher traffic pool, but it may be recommended again in the future;

2. If his friends are interested and like, forward or comment on the video, the recommendation mechanism will be triggered. When multiple friends comment together, the content will enter a larger traffic pool and obtain a higher weight, thus increasing the chance of being recommended.

3. The big accounts that are frequently followed and browsed by friends who frequently contact, chat and interact will also be recommended to friends. In this way, the secondary relationships based on acquaintance social interaction will generate fission.

Although the entire recommendation process of the video account is based on social interaction among acquaintances, the content presented after being recommended is, on the contrary, an unfamiliar social environment. We don’t know who will appear in the video account, which friend is interested in it, or whether we are interested in it.

From this, we can judge that the social weight of the current video account recommendation mechanism is relatively large. Under such a recommendation mechanism, those who have a large number of friends, multiple communities, and public accounts will become the focus of helping the marketing and promotion of video accounts, and are also likely to become top accounts more easily than ordinary people.