A common sense about content distribution is that no one distribution category email list method is necessarily more efficient than another, and distribution efficiency must be judged in combination with content form characteristics, main user portraits, and scene requirements. When I was young and ignorant, when I talked about content distribution, I always came up with "I think social recommendation/subscription is better", and even confused the concept of "information category email list flow/algorithmic recommendation/recommendation".
Looking back now, I find that many of the "judgments" at that time category email list were unreasonable. For example, after I first came into contact with Douyin, I hated "recommendation" very much, because I thought the quality of the recommended content was poor and uninteresting, and it was difficult to train. But the reason is that on the one hand, the accuracy of the content category email list algorithm needs to be improved, and the recommendation strategy needs to be improved.
On the other hand, the interactive form of Douyin makes it difficult to category email list accurately judge my attitude towards a certain video from my behavior data. Open a brain hole, if a short video product can clearly set the theme of the recommended content, can independently adjust various recommended parameters (strategies), and can directly obtain user feedback by swiping left and right (Hmmm...) or even hitting stars. Content evaluations are then directly reflected in subsequent category email list content recommendations (data).