And, of course, not all content can rise quickly on recommendation. For example, a financial and economic account, long-term bulk drying, data analysis depth is sufficient, but the recommended amount has not been satisfactory. In analysis, many netizens feel that the content is “too hard to understand” and are afraid that it will not be understood. The platform has also found that a large number of recommendations within a short period of time would also result in parts being miscalculated as machines, instead limiting further traffic. Thus, while the interactive recommendations are valid, their content needs to be adapted to popular taste and accessible。
There is also a trend behind the interactive recommendations, with more and more platforms showing publicly the data on the “recommended” buttons so that netizens know how many people are affected by what they support. For example, in a community of knowledge questions and answers, the impact list is updated in real time to see how many new users have joined. Such transparency increases the sense of participation and inspires more ordinary people to come forward and recommend new knowledge。
However, the referral mechanism also faces new problems. Some content now begins to be deliberately “recommended”, and the author continues to remind you at the end of the video or article to “point recommendations” and use them to attract users. In the short term, it is true that exposure can be facilitated, but the content itself is not of the same quality and makes it easier for some of its members to repulsive. Some data show that if content is recommended and nothing is done, netizens do not buy and the number of recommendations decreases. This phenomenon was noted by the platform's operators at an early stage, adjusting the recommended weight allocation to ensure that it was not recommended that it would be easy。
Ultimately, the referral mechanism makes the quality of content more important. Netizens recommend that recognition of the usefulness and clarity of knowledge and the response of platform algorithms give the content itself the opportunity to be seen more widely. This change makes it no longer an accident to “breed in knowledge”, in which everyone can participate and where quality content can enter the public view from a small circle。
As can be seen, the knowledge-based content has begun to move in a new fashion through interactive recommendations. During the first two years, the cope and skills platforms have grown rapidly in south-east asia, relying on a large number of users who offer to share. In indonesia, for example, a website on science and technology education allows users to light up their knowledge, attract millions of visitors in a short period of time and increase the diversity of content and participation in parallel. In contrast to the japanese-korean market, knowledge content is mainly subject to official editing and referral mechanisms are limited, resulting in some quality content being disseminated only in small circles. This disparity has forced local platforms to reassess the practical impact of the interactive recommendations。
At the moment, many of the country's own media accounts are trying to make an interactive recommendation. Whether it be history, psychology, science, or life skills, authors are more focused on expressing themselves clearly and seeking public understanding. Netizens recommend, not just for personal pleasure, but for content upgrading. Platform data indicate that the overall rate of recommendation for knowledge content has increased by 30 per cent over the past year, leading to an increase in the rate of increase in relevant accounts。
The problem is that the referral mechanism needs to be improved. Will content platforms be recommended for manipulation? Will user recommendations be delayed by algorithms? All this is to be observed. However, the overall trend is clear: interactive recommendations involve ordinary people in content screening and knowledge dissemination becomes more democratic. Previously, it had to wait for algorithms, and now we can move forward with valuable knowledge。
So, what is the next step? Will the platform combine referral and reward mechanisms to encourage more people to discover cold knowledge and new skills? Will the initiative of netizens affect the direction of content creation? Knowledge-sharing will be much broader in the future as long as the `lighting recommendations' mechanism continues to innovate. For those who like to raise their knowledge, recommendations have become a small habit, and perhaps soon we will see more high-quality knowledge in different fields on the content platform, moving into everyday life。
