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 |
| 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 |
