JuliaCon 2019

Recommendation.jl: Building Recommender Systems in Julia @ JuliaCon 2019

Abstract

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.

Description

Recommendation.jl allows you to easily implement and experiment your recommender systems, by fully leveraging Julia's efficiency and applicability. This talk demonstrates the package as follows.

The speaker first gives a brief overview of theoretical background in the field of recommender systems, along with corresponding Recommendation.jl functionalities. The package supports a variety of well-know recommendation techniques, including k-nearest-neighbors and matrix factorization. Meanwhile, their dedicated evaluation metrics (e.g., recall, precision) and non-personalized baseline methods are available for your experiments.

Next, this talk discusses pros and cons of using Julia for recommendation. On the one hand, a number of algorithms fits well into Julia's capability of high-performance scientific computing in this field, but at the same time, it is challenging to make Julia-based recommenders production-grade at scale. The discussion ends up with future ideas of how to improve the package.

We will finally see the extensibility of the package with an example of building our own custom recommendation method. In practice, Recommendation.jl is designed to provide separated, flexible data access layer, algorithm layer, and recommender layer to the end users. Consequently, the users can quickly build and test their custom recommendation model with less efforts.

Reference: Recommendation.jl: Building Recommender Systems in Julia, an article written by the speaker.

Slides

Video

  書いた人: Takuya Kitazawa（たくち）

長野県出身、カナダ・バンクーバーを拠点に活動するソフトウェアエンジニアです。10年以上にわたりB2B/B2Cの各領域でWeb技術・データサイエンス・機械学習のプロダクト化および顧客への導入支援・コンサルティングに携わってきました。現在は独立し、アフリカ・アジア・北米の企業や個人を対象に、テクノロジー戦略策定や倫理的AI実装をお手伝いしています。アフリカのマラウイでは現地企業のICTディレクターとして、デジタル・トランスフォーメーションを推進中。詳しい経歴はCV を参照ください。いろいろなまちを走って、時に自然と戯れながら、その時間その場所の「日常」を生きています。ご意見・ご感想およびお仕事のご相談は [email protected] まで。