为了提升用户阅读体验，信息流产品是如何避免给用户推荐重复内容 ...
zhuanlan.zhihu.com
为了提升用户阅读体验，信息流产品是如何避免给用户推荐重复内容 ...
When YouTube got to millions of videos, it got hard to find what you wanted to watch.” Yes, this is a fancy problem for a networked product but it’s exactly the problem that YouTube faced as it grew.
Andrew Chen • The Cold Start Problem: How to Start and Scale Network Effects
Whereas in social media, people see content from their friends regardless of the quality of the content, in recommendation media, content distribution is optimized for engagement. This results in very little waste in a feed, and consumption patterns are highly efficient.
Michael Mignano • The End of Social Media and the Rise of Recommendation Media
Upon reaching an equilibrium between free and paid content, creators and communities face another dilemma. Their revenue correlates directly with how much content they can produce. But reapplying that method forever would mean constantly increasing content output until every waking hour was devoted to production. Beyond sounding exhausting, studies... See more
Pallet • Escaping the Content Treadmill
Even with the best recommendation algorithm, only a fraction of your feed will be content that you truly enjoy. For example, the average ratio of hearts to views on TikTok is roughly 5% . People are just not that predictable. On YouTube, every time you select a video but then decide you don’t want to watch it, it’s an annoyin
... See more