Recommendation Systems
MK and
Recommendation Systems
Status Traps: Learning from Web2 Social Networks - a16z crypto
Andreessen Horowitz (AZ)a16zcrypto.com
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
Towards Recommender System Optimization: Our Data Tool for Algorithmic Optimization on Spotify [Part 1]
Dmitry Pastukhovmusic-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)
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
Constructor: Holistic Product Discovery for Ecommerce Results
constructor.io
How Recommendation Algorithms Actually Work | Future - https://future.com/forget-open-source-algorithms-focus-on-experiments-instead
Thomas Dimsonfuture.com
Matt Ross and