End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
arxiv.orgSaved by Sam Liebeskind
End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting
“We all consume info differently: I like narrative essays, cofounder likes thematic sections, PM likes bullet lists.
@diabrowser’s “Filters” convert webpages into your ideal versions of them. Think Instagram Filters for Tabs.
Or AI Reader Mode for you!”
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Josh Millerx.comToday 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
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