Scope
Created Recommendation.jl, a Julia package for building recommender systems that leverages Julia's high-performance scientific computing capabilities. Designed with extensibility in mind through separated data access, algorithm, and recommender layers.
Technology
- Language: Julia
- Algorithms: k-Nearest Neighbors, Matrix Factorization, Non-personalized Baselines
- Evaluation: Recall, Precision, and other dedicated recommendation metrics
- Architecture: Flexible three-layer design (data access, algorithm, recommender)
Key Features
- High-Performance Computing: Leverages Julia's efficiency for scientific computing in recommendation algorithms
- Easy Experimentation: Enables quick implementation and testing of recommender systems
- Extensibility: Separated layers allow users to build custom recommendation models with minimal effort
- Comprehensive Toolkit: Includes algorithms, evaluation metrics, and baseline methods
Presentation
- JuliaCon 2022: Recommendation.jl: Modeling User-Item Interactions in Julia [Slides] [Video]
- JuliaCon 2019: Recommendation.jl: Building Recommender Systems in Julia [Slides] [Video]
Learn More
- Package: Recommendation.jl
- Articles: Building Recommender Systems in Julia
Author: Takuya Kitazawa
I am a product builder, mentor, and advocate for sustainable technology development with a decade of experience in AI/ML products, data systems, and digital transformation. Based in Canada and originally from Japan, I have lived and worked globally, including part-time residence in Malawi, Africa. Visit my portfolio to learn more about my work, or reach out to me at [email protected].