Recently, there's been a pretty interesting topic. A major influencer announced on social media that they plan to open-source the new platform's algorithm within 7 days, covering all code related to content recommendation and ad placement. This process will be repeated every four weeks, accompanied by detailed developer documentation.

This in itself sounds good, and transparency has increased. But some users have raised a real pain point: why is the logic of the recommendation page so strange? If you casually like or browse a post outside of your usual interests, you'll be bombarded with similar content one after another. Conversely, the accounts you follow or the topics you actively participate in are hard to see again.

This kind of recommendation bias has indeed troubled many people. Of course, algorithms need to consider users' curiosity and exploration, but if they are too sensitive, user experience can easily backfire. Finding the balance between content diversity and personalized recommendations might be the real challenge.