# asmuth/recommendify

Generate recommendations using collaborative filtering

Repository: https://github.com/asmuth/recommendify
Canonical: https://ross.abutalabs.com/products/recommendify
Language: Ruby
License Family: other
Last push: 2014-05-08T21:18:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5330, "days_push": 4500, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1649, forks 145 (observed 2026-08-28T04:05:16.782591+00:00)

## What it is
Recommendify is a Ruby/Redis-based recommendation engine that generates item-to-item recommendations using collaborative filtering. It processes user interaction data (e.g., user-item pairs) incrementally and across multiple hosts, with a worker implemented in plain Ruby and native C.

## Use cases
- generate 'users who bought this also bought that' recommendations from purchase data
- recommend similar videos based on co-viewing behavior
- build item-to-item similarity matrices from user interaction sets
- compute jaccard-based recommendations incrementally in Ruby
- power product recommendations for an e-commerce site

## When to choose
- you need item-to-item collaborative filtering in a Ruby stack
- your interaction data fits a co-concurrence model and you want Redis-backed incremental updates
- you want a lightweight, self-hosted recommender without heavy ML dependencies

## When to avoid
- you need actively maintained software with a license and recent updates
- you need content-based or deep-learning recommendations
- your stack is not Ruby/Redis
- you need large-scale distributed training beyond simple co-concurrence

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, search-engine, caching
- domain: machine-learning, e-commerce, data-science
- platform: ruby
- tags: collaborative-filtering, recommendation-engine, redis, similarity-matrix, jaccard, linux, macos

## Member repositories
- asmuth/recommendify (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.782591+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:07.737227+00:00, confidence not recorded.
  - readme: https://github.com/asmuth/recommendify (fetched 2026-08-28T04:05:16.782591+00:00, sha fa1a0650e6aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.