Home  >   Blog  >   Recommendation.jl Came Back to #JuliaCon 2022

2022-08-06

Recommendation.jl Came Back to #JuliaCon 2022

  This article is part of the series: Building Recommender Systems in Julia

At JuliaCon 2022 @ Online held during the last week of July, I gave a lightning talk about Recommendation.jl, a Julia package for building recommender systems. It's been 3 years since the last time I talked about the package at JuliaCon 2019, and, since polishing the (outdated) implementation towards v1.0.0 is one of my recent focus areas, I decided to showcase the updates of the package and take it as an opportunity to review the remaining steps ahead of me.

Check out the presentation at YouTube:

Over the last few months, my biggest technological interest has been in what defines the "goodness" of data-driven applications, including recommender systems. Intuitively, more accurate prediction is better as algorithmic recommendation eventually encourages the users to "efficiently" use their time on the applications. However, it often causes unintended consequences as we've discussed in the context of ethical product development, data science ethics, and humane use of technology. Thus, I do believe non-accuracy aspects of the systems are equally or even more important, and I'm glad that I was able to turn the idea into actual implementation as part of the Julia package.

The topics I highlighted in the talk mostly overlap with the following articles that I posted early this year:

I wish I could discuss more about each of these concepts in the talk, but stay tuned for now - As mentioned, I'm planning to write a JuliaCon proceeding paper in the coming months so that I can provide in-depth explanation, discussion, and evaluation results.

Last but not least, the online conference experience of JuliaCon 2022 was superb. During my talk, I simply needed to make myself available in a dedicated Discord channel, and Q&A happened there:

juliacon-2022-discord

(Yes, I was down for COVID when I recorded the talk...)

I would like to thank organizers for the hassle-free video recording/uploading process and well-organized "virtual venue" on Discord. Similarly to my previous experience at the physical conference in 2019, it is clear how powerful & supportive the Julia community is.

  This article is part of the series: Building Recommender Systems in Julia

  Share


  Categories

Programming Conference Recommender Systems

  See also

2022-04-03
Cross Validation for Recommender Systems in Julia
2022-03-06
Serendipity: It's Relevant AND Unexpected
2019-07-26
Lightning Talk about Recommender Systems in Julia at #JuliaCon 2019

  More

Last updated: 2022-09-02

  Author: Takuya Kitazawa

Takuya Kitazawa is a freelance software developer based in British Columbia, Canada. As a technologist specializing in AI and data-driven solutions, he has worked globally at Big Tech and start-up companies for a decade. At the intersection of tech and society, he is passionate about promoting the ethical use of information technologies through his mentoring, business consultation, and public engagement activities. See CV for more information, or contact at [email protected].

  Now   Gift a cup of coffee

  Disclaimer

  • Opinions are my own and do not represent the views of organizations I am/was belonging to.
  • I am doing my best to ensure the accuracy and fair use of the information. However, there might be some errors, outdated information, or biased subjective statements because the main purpose of this blog is to jot down my personal thoughts as soon as possible before conducting an extensive investigation. Visitors understand the limitations and rely on any information at their own risk.
  • That said, if there is any issue with the content, please contact me so I can take the necessary action.