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Discovering Things We Truly Love
Rather than an algorithmic feed with random products, the app should feature collections of products that certain people use or recommend. Apps like Svpply and Kit have tried to build similar product recommendation services, but none of them have ever gained mainstream adoption. Yet I’m still convinced that there is a market for a stand-alone app t... See more
Julian Lehr • What Shopify and Amazon can learn from Mimetic Theory
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 “fu
... See moreNathan Baschez • Noise-Canceling Filters for the Internet are Coming
Relying on creatives or niche experts (e.g., wine or book editors) is not a novel concept, though I do think we’re leaning on them more heavily as an antidote to algorithmic recommendations, and I know they’re working harder to produce differentiated suggestions.
Michelle Rose Joseph • No. 13 — Reclaiming Discovery From the Algorithms
There are four reasons for why “Recommendations is personalisation”