# srendle/libfm

Library for factorization machines

Repository: https://github.com/srendle/libfm
Canonical: https://ross.abutalabs.com/products/libfm
Language: C++
License: GPL-3.0
License Family: copyleft
Last push: 2020-03-28T07:37:04+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": 4371, "days_push": 2349, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1487, forks 408 (observed 2026-08-28T04:04:52.064705+00:00)

## What it is
libFM is a C++ library implementing factorization machines, a model that combines feature engineering with factorization models to estimate interactions between categorical variables. It supports SGD, ALS, and MCMC (Bayesian inference) optimization and ships as a command-line tool.

## Use cases
- train factorization machine models on categorical feature data
- build recommendation and click-through-rate prediction models
- predict ratings from sparse user-item interaction data
- run Bayesian inference with MCMC for factorization machines
- model pairwise feature interactions without manual feature engineering

## When to choose
- you need factorization machines with SGD, ALS, or MCMC training
- your data has large sparse categorical feature spaces
- you want a proven, well-cited implementation for CTR or recommender tasks

## When to avoid
- you need a Python-native or GPU-accelerated ML workflow
- you require active development, recent releases, or community support
- you need deep learning or neural recommender models

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, math
- domain: machine-learning, data-science
- platform: cpp, cli
- tags: factorization-machines, sgd, als, mcmc, recommendation, categorical-features, algorithms, linux

## Member repositories
- srendle/libfm (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.064705+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 2026-08-30T04:33:49.018039+00:00, confidence not recorded.
  - readme: https://github.com/srendle/libfm (fetched 2026-08-28T04:04:52.064705+00:00, sha ea599c9f138c)
- Data as of 2026-08-30T08:39:29.467469+00:00.