Building Recommender Systems in Julia

While I was studying recommendation algorithms in my master's program, I happened to know the Julia programming language. It focuses on high-performance scientific computing by utilizing the just-in-time compiler, and I see the programming language can be a great choice for developers to efficiently and effectively pre-process user-item data, build a recommendation model, evaluate a ranked list of recommended contents, and post-process the recommendation if needed. Thereafter, I have been implementing various recommendation techniques in Julia and packaging them as Recommendation.jl.


After presenting at JuliaCon conferences twice in 2019 and 2022, I'm finally working on a publication for its proceeding. A paper review process is underway at JuliaCon/proceedings-review.

Articles in this series

  1. Recommendation.jl: Building Recommender Systems in Julia
  2. Publishing My Master's Thesis with Documenter.jl
  3. Lightning Talk about Recommender Systems in Julia at #JuliaCon 2019
  4. Recommendation.jl v0.4.0: Working with Missing Values, Data Typing, and Factorization Machines
  5. Cross Validation for Recommender Systems in Julia
  6. Recommendation.jl Came Back to #JuliaCon 2022

  Author: Takuya Kitazawa

Takuya Kitazawa is a freelance software developer, previously working at a Big Tech and Silicon Valley-based start-up company where he wore multiple hats as a full-stack software developer, machine learning engineer, data scientist, and product manager. At the intersection of technological and social aspects of data-driven applications, he is passionate about promoting the ethical use of information technologies through his mentoring, business consultation, and public engagement activities. See CV for more information, or contact at [email protected].