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 ...
-Focus on content types with lower replay value
Harvard Business Review • Building the Middle Class of the Creator Economy
sari added
These gray-area business logic questions where recommender systems become just as much of an art as a science, continuing to generate potential content for recommendation, blending it, filtering out negative or prohibited content on the platform to get to a “good” feed of engageable content.
Vicki Boykis • What we talk about when we talk about The Algo
sari added
Doing so always imposes some additional friction on the content creator, but the benefit is breaking one monolithic feed into more distinct units, allowing users the ability to shift gears mentally by seeing and anticipating the structure, much like how a magazine is organized.
Eugene Wei • Invisible Asymptotes
Sterling Proffer and added
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 moreknightcolumbia.org • TikTok’s Secret Sauce
Despite TikTok’s design innovations being well known, other apps have trouble copying them because they were originally designed for a very different experience, and they are locked into it due to their users’ and creators’ preferences. This is a classic example of the innovator’s dilemma
knightcolumbia.org • TikTok’s Secret Sauce
A central challenge for any recommendation algorithm is the tradeoff between safe but somewhat boring recommendations that are similar to recommendations that worked well in the past (“exploitation”), and risky recommendations that are unlikely to be good but have a high payoff if they do turn out to be good (“exploration”). Explorat
... See moreknightcolumbia.org • TikTok’s Secret Sauce
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
Lillian Sheng and added