# guoguibing/librec

LibRec: A Leading Java Library for Recommender Systems, see

Repository: https://github.com/guoguibing/librec
Canonical: https://ross.abutalabs.com/products/librec
Homepage: https://www.librec.net/
Language: Java
License: NOASSERTION
License Family: other
Topics: recommender-systems, recommendation-algorithms, collaborative-filtering, matrix-factorization, tensor-factorization, probabilistic-graphical-models, recommender, systems, factorization, matrix, tensor, collaborative, filtering, sparse, java
Last push: 2023-07-13T17:03:11+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4619, "days_push": 1147, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3257, forks 1018 (observed 2026-08-28T04:07:52.416101+00:00)

## What it is
LibRec is a Java library for recommender systems implementing over 70 state-of-the-art recommendation algorithms. It supports the two classic recommendation tasks of rating prediction and item ranking with a modular architecture covering data splitting, conversion, similarity, algorithms, evaluators, and filters.

## Use cases
- build a movie recommendation engine in Java
- run collaborative filtering experiments on rating datasets
- compare matrix factorization algorithms for rating prediction
- generate top-N item rankings for users
- benchmark recommender algorithms on sparse datasets
- prototype a custom recommendation algorithm

## When to choose
- you need a mature Java library with a wide range of recommendation algorithms
- you want to evaluate and compare recommender models with built-in evaluators
- your stack is JVM-based and you need rating prediction or item ranking

## When to avoid
- you need deep learning based recommenders or GPU acceleration
- you want a production recommendation service rather than an algorithm library
- you work primarily in Python and prefer libraries like Surprise or implicit

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, search-engine
- domain: machine-learning, data-science
- platform: jvm, cross-platform
- tags: recommender-systems, collaborative-filtering, matrix-factorization, tensor-factorization, rating-prediction, item-ranking, algorithms

## Member repositories
- guoguibing/librec (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:52.416101+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 2026-08-30T07:23:15.374510+00:00, confidence not recorded.
  - readme: https://github.com/guoguibing/librec (fetched 2026-08-28T04:07:52.416101+00:00, sha be6b1b246a39)
  - homepage: https://www.librec.net/ (fetched 2026-08-29T09:36:46.931274+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.