#Recommenders
Live, measured metrics for the hashtag #Recommenders from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #recommenders
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-07-28 04:30 UTC1 uses by 1 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-07-28 04:30 UTCLive pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-07-28 04:30 UTCEverything below is measured over the latest 9 public posts (spanning ~32606 hours).
Posting hours (UTC)
Languages: English (6) · German (1) · Russian (1) · French (1)
Avg boosts / post: 1.1
Top of the latest posts
📣 Want to curate future research in a #publishing system that ISN’T broken / exploitative / for-profit? @pcirr is #RECRUITING #RECOMMENDERS (#editors) from ALL fields! 👉 We seek post-PhD independent researchers w at least a handful of 1st
In digital marketplaces, recommender engines are shifting from click-chasing to delivering real value, relying on stronger signals like trust, social networks, and sustained user satisfaction. Scaling meaningful engagement is difficult and
Ohai! The first paper for my dissertation has just been accepted to @ACM :) If you enjoyed my feminist rants on technology, you might enjoy it's academic version, too! "From Good Intentions to Meaningful Impact: A Design Justice Approach fo
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/recommenders