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Resources for building better recommender systems

 

Building recommenders isn’t always easy. With input from Jacopo Tagliabue, Ronay Ak from Nvidia, and Serdar Kadioglu from Fidelity, here’s a list of resources that can help.

Learn more from the webinar on The Era of Hyper-Personalization: Building Better Recommender Systems and be sure to join the Comet ML Slack community for any questions!

 

Nvidia Merlin An open source framework for building high-performing recommender systems

RecList An open source library for behavioral, “black-box” testing for recommender systems

Fidelity Mab2Rec An open source framework for building contextual multi-armed bandits recommenders

RecSys Reproducibility Paper at TMLR’22 D. Kilitçioğlu, S. Kadıoğlu, Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It, Transactions on Machine Learning Research (TMLR) 2022

https://openreview.net/pdf?id=sX9d3gfwtE

Association for Computing Machinery (ACM) Terminology on Reproducibility https://www.acm.org/publications/policies/artifact-review-and-badging-current
Contrastive language and vision learning of general fashion concepts
Companies, people and communities to follow
Conferences

 

Claire Pena

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