Nonlinear Latent Factorization for Recommender Systems (feat. Condé Nast)

New York City Campus

GA NYC (Manhattan), Classrooms
10 East 21st Street
New York NY 10010

Past Locations for this Event

Nonlinear Latent Factorization for Recommender Systems (feat. Condé Nast) | New York City

New York City Campus

GA NYC (Manhattan), Classrooms
10 East 21st Street
New York NY 10010

Past Locations for this Event

About this event

Please note in order to gain entry, you MUST sign up on both this site and Dataiku's MeetUp page

Tentative Schedule:

  • 6:30pm: Pizza + Beer networking
  • 7:00pm: TBD by Data Scientist at Dataiku
  • 7:30pm: Nonlinear Latent Factorization for Personalized Recommender System by TaeYoung Choi, Data Scientist at Conde Nast
  • Talk Abstracts:

    Nonlinear Latent Factorization for Personalized Recommender System by TaeYoung Choi, Data Scientist at Conde Nast The New Yorker publishes about 90 new articles every week and about half of the pageviews are made within the first 24hrs. As The New Yorker is renowned for its political and cultural commentary, its articles in general have shorter lifespans compared to other items such as IPhone cases on Amazon or Bad Guy by Billie Eillish on Spotify. In other words, we do not have time to gather enough data - we are constantly facing the cold-start problem. This talk will cover how I approached the problem by creating user embeddings that will allow us to make recommendations as soon as a new article gets published. The model burrows the famous word2vec architecture and contains additional layers that incorporate nonlinearity to capture multiple interests for our readers.

    Speaker bio: TaeYoung a data scientist at Condé Nast, home to some of the world's most iconic brands, including Vogue, The New Yorker, GQ, Vanity Fair and Wired. At Condé Nast, he has led various data science projects such as traffic forecasting, subscription churn, user segmentation and classification, and content personalization. He's passionate about personalization and recommendation. He's previously participated in developing a data science pipeline at MyCelebs, (a Korean-based data science AI start-up), and worked as a graduate researcher at Columbia Business School, building matching algorithms from unstructured data for Executive Education Program.

    Coming up near you

    Let’s Keep You Updated

    Enter your email to start following

    By providing us with your email, you agree to the terms of our Privacy Policy and Terms of Service.