Recommender Systems Input data undergoes a series of vector/matrix computations item user value Rating, purchase, click, watch, … Price, category, … Demographics, … Event Matrix Recommender Top-k recommendations …
Recommender Systems Lifecycle that Recommendation.jl makes it easier to follow Data Pre-process Build Evaluate Post-process How to build recommender Intro to recommender algorithms Why Julia • Vector/matrix-friendly syntax
Recommender Other Tools How OSS communities design recommender systems Language Data modeling and linear algebra Characteristics MyMediaLite C# Built-in arithmetic operators with fi le IOs Simplicity and transparency of basic recommendation techniques LibRec Java Custom interfaces (e.g., dense/sparse matrices) & built-in data structures Wide range of algorithms implemented from scratch LensKit / LightFM Python NumPy/SciPy & Cython Rapid development and wider use cases in the Python community Recommendation.jl Julia Built-in vector/matrix representations Take full advantage of built-in o ff ering for simplicity and e ff i ciency Data model Algorithm Interface Metrics Utils