Surprise
/All repositories
Overview
HEALTH SCORE
Concerning*
Partial score (2/3 categories)
31out of 65
Maintainer coverage is the lead risk. Development cadence is present but not enough to offset the structural risk. Security & Supply Chain is unavailable, so weights were redistributed.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Concerning*(31/65)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
No PRs or issues were opened in the review window, so response time could not be measured.
Bus factor
Only 1 active maintainer currently has merge rights.
Organization Diversity
Contributors span 40 organizations.
Project Achievements
Leaderboards
About
Surprise is a Python scikit for building and analyzing recommender systems, providing tools for rating prediction, dataset handling, and evaluation of recommendation algorithms. It implements various collaborative filtering approaches and allows easy comparison of algorithm performance.
First commit
Software value
$327K
Tags
factorization
machine-learning
matrix
recommendation
recommender
svd
systems
License(s)
BSD-3-Clause
Data sources
Git
GitHub