Takuya Kitazawa, a senior engineer at Arm Treasure Data, is passionate about bridging a gap between scientific theory and real-world practice in the industry. At the organization building an enterprise-grade big data analytics platform, he has been practically acted as a data scientist, technical evangelist, consultant, machine learning engineer, and software engineer through the experience of contributing to Apache Hivemall, implementing out-of-the-box ML application, presenting at conferences, and working on a variety of customer-facing opportunities. His current interest is particularly in large-scale ML and its UI/UX matter, especially in the context of recommender systems and data streams.
This talk demonstrates Recommendation.jl, a Julia package for building recommender systems. We will eventually see (1) a brief overview of common recommendation techniques, (2) advantages and use cases of their Julia implementation, and (3) design principles behind the easy-to-use, extensible package.