# ocelma/python-recsys

A python library for implementing a recommender system

Repository: https://github.com/ocelma/python-recsys
Canonical: https://ross.abutalabs.com/products/python-recsys
Language: Python
License Family: other
Last push: 2020-12-29T07:08:13+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5441, "days_push": 2073, "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 1480, forks 430 (observed 2026-08-28T04:04:50.896025+00:00)

## What it is
A Python library for building recommender systems, built on top of Divisi2, NumPy, and SciPy. It provides SVD-based matrix factorization for computing item similarities, predicting ratings, and generating recommendations.

## Use cases
- build a movie recommendation engine in python
- compute similarity between items using svd
- predict user ratings for items
- recommend items to users with collaborative filtering
- run matrix factorization on movielens dataset
- find similar movies to a given movie

## When to choose
- you need a simple python library for svd-based recommender systems
- you are learning or prototyping collaborative filtering algorithms
- you work with rating datasets like movielens

## When to avoid
- you need a maintained library with recent updates or support
- you need production-scale recommendation systems
- you need deep learning based recommenders
- you use python 3 exclusively since the library targets legacy python 2 tooling

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, data-science
- domain: machine-learning, data-science
- platform: python
- tags: recommender-system, collaborative-filtering, svd, matrix-factorization, movielens, algorithms

## Member repositories
- ocelma/python-recsys (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:50.896025+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 2026-08-30T04:34:15.633281+00:00, confidence not recorded.
  - readme: https://github.com/ocelma/python-recsys (fetched 2026-08-28T04:04:50.896025+00:00, sha 703282172dad)
- Data as of 2026-08-30T08:39:29.467469+00:00.