Recommendation Systems
MK and
Recommendation Systems
Towards Recommender System Optimization: Our Data Tool for Algorithmic Optimization on Spotify [Part 1]
Music Tomorrowmusic-tomorrow.com![Thumbnail of Towards Recommender System Optimization: Our Data Tool for Algorithmic Optimization on Spotify [Part 1]](https://uploads-ssl.webflow.com/6206e1343aa2f122195717f8/6310b1a1447a22ac785a50ae_alina-grubnyak-ZiQkhI7417A-unsplash%20(1).jpg)
In the rush to copy TikTok, many Western social networks have misread how easy it is to apply lessons of a very particular short video experience to social feeds built around other formats. If you’re Instagram Reels and your format and interface are a near carbon copy, then sure, applying the lessons of my three TikTok essays is straightforward.... See more
Eugene Wei • How to Blow Up a Timeline
How Recommendation Algorithms Actually Work | Future - https://future.com/forget-open-source-algorithms-focus-on-experiments-instead
future.comfuture.com
Matt Ross and
as a network scales, it is inevitable that you need a way to sort & rank content.
web 2.0 solved it by ranking for popularity but a better approach could be to have a minimal quality threshold but not to try to rank things above that - so the search is not maximization-driven and leaves more room for serendipity.
h/t ken stanley
Today most algorithms that recommend or suppress content act purely on the basis of inferred popularity. They look at how much time people spend engaging with a piece of content, and boost it to more people if the numbers look good. The content itself is almost purely a black box. Some algorithms try to classify content with tags like “food” or
... See moreNathan Baschez • Noise-Canceling Filters for the Internet are Coming
