#recommendations
Live, measured metrics for the hashtag #recommendations from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #recommendations
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 · fosstodon.org (Mastodon public tags API) · fetched 2026-11-08 10:44 UTC4 uses by 4 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-11-08 10:44 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-11-08 10:44 UTCEverything below is measured over the latest 40 public posts (spanning ~1429 hours).
Posting hours (UTC) — busiest: 13:00
Languages: English (38) · German (2)
Avg boosts / post: 2.3
Top of the latest posts
Recommendations for a solid VPN that isn't Proton? My subscription is about to come to an end so maybe it's time to move on. #vpn #recommendations
My dad (who never asks for gifts) has made it known he wants a digital photo frame as a holiday gift this year. I'm doing my own research here, but I'd love recommendations if anyone has them! A couple key requirements: 1. Must be functiona
RE: https://chaos.social/@ishotjr/117525847922260570 I'm also looking for an inexpensive, simple #hosting provider for static HTML (no, not GitHub pages or anything related to #BigTech) -- any #recommendations? 💗
What “recommendations” means
Wiktionary · WikipediaA recommender system, also called a recommendation engine or content discovery platform is a type of information filtering system that aims to suggest items most relevant for some input or to a particular user. In the context of social media, search engines, and other online services, a recommender system for a given service might sometimes informally but erroneously be referred to as the service's "algorithm". The use of recommender systems is pervasive, with commonly recognised examples includ
“Recommender system” on Wikipedia (CC BY-SA) →#recommendations across platforms
every network with a public tag surfaceFollow #recommendations straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
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/recommendations