Скачать или смотреть Recommendation.jl: Modeling User-Item Interactions in Julia | Takuya Kitazawa | JuliaCon 2022

Recommender system is a data-driven application that generates personalized content for users. This talk shows how Julia can be a deeply satisfying option to capture the unique characteristics of recommenders, which rely heavily on repetitive matrix computations in multi-stage data pipelines. To build trustworthy systems in terms of not only accuracy and scalability but usability and fairness at large, we particularly focus on API design and evaluation methods implemented on Recommendation.jl.

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