# \[ANN\] v0.1 Canapes.jl: Sparse statistical learning and recommender systems in pure Julia

**URL:** <https://discourse.julialang.org/t/ann-v0-1-canapes-jl-sparse-statistical-learning-and-recommender-systems-in-pure-julia/139763>\
**Category:** Package Announcements\
**Tags:** package, recommendations, sparse, factorization\
**Created:** [September 30, 2026, 6:28pm UTC](https://discourse.julialang.org/t/ann-v0-1-canapes-jl-sparse-statistical-learning-and-recommender-systems-in-pure-julia/139763 "2026-09-30T18:28:49Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![AbrJA](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abrja/32/207387_2.png) [@AbrJA](https://discourse.julialang.org/u/AbrJA)\
**Post date:** [September 30, 2026, 6:28pm UTC](https://discourse.julialang.org/t/ann-v0-1-canapes-jl-sparse-statistical-learning-and-recommender-systems-in-pure-julia/139763/1 "2026-09-30T18:28:49Z")

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I’m excited to announce the v0.1.x of **[Canapes.jl](https://github.com/AbrJA/Canapes.jl)**, a pure-Julia package designed for statistical learning on sparse matrices. It brings matrix factorization, item-item similarity, low-rank completion, and sparse regression.

Recommender systems and collaborative filtering have lacked maintained, production-ready tooling with existing options often outdated or unmaintained. This library aims to close that gap by creating a high-performing library competing with (python) implicit, (R) rsparse and others well know libraries.

The package is still work in progress, so any feedback or contribution is very welcome. I hope it helps to mature the ecosystem for recommendation engines and we can take Julia to the next level!