After identifying the user
also tap the sustainability of content release for creativity, which has a relatively benign effect. Possible author Content-driven, complete transformation. For possible authors, users at this stage are in the swing period from users to authors. Users in this period need to be stimulated by corresponding interest points and find track KOLs that they can imitate or hope to engage in to solve a problem. Interest point stimulation and content release direction and "homework" are two issues. Typically, a recommendation system requires several things that are important atthis stage of the user's life: Accurately identify and mine Afghanistan WhatsApp Number such users: How to identify such users can usually rely on manual and algorithmic rules, such as attribution and feature statistics through pre-behavior data and basic data of novice authors. It can be concluded relatively accurately what kind of users will become authors. Provide appropriate interest incentives: , the recommendation system can provide interest incentives at this stage, such as what are the benefits of becoming an UP owner and how much money does it cost to
https://lh7-us.googleusercontent.com/JjH4RAl-EokWHCSy2HweF7RoxmLQsRn5n3q-sUhh4MO80pnMjEEd0B0WO8tCjDkN0C7zBva0DYG5QdjqBkd-RzzdxvgoizVVi_s1-WUE3vtq_RKF5vfFBvWcv0hNiOEJwCQV2RsdeqXi5mS1
be an UP owner, and gradually try to change the user's role? Generally speaking, different types of users have different interests. Some users have financial interests and some have data interests. It is feasible for the recommendation system to conduct personalized or multi-interest exploration in this scenario. Social stimulation: Xiaohongshu-type communities usually have certain social attributes and a certain herd mentality. By distributing user content within their social scope to users, it will also have a certain stimulating effect. Provide suitable track KOLs as
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