{"id":4758,"date":"2022-11-21T15:09:36","date_gmt":"2022-11-21T23:09:36","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=4758"},"modified":"2025-04-24T17:16:22","modified_gmt":"2025-04-24T17:16:22","slug":"resources-for-building-better-recommender-systems","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/resources-for-building-better-recommender-systems\/","title":{"rendered":"Resources for building better recommender systems"},"content":{"rendered":"\n

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Building recommenders isn’t always easy. With input from Jacopo Tagliabue<\/a>, Ronay Ak<\/a> from Nvidia, and Serdar Kadioglu<\/a> from Fidelity, here\u2019s a list of resources that can help. <\/span><\/p>\n\n\n\n

Learn more from the webinar on The Era of Hyper-Personalization: Building Better Recommender Systems<\/a> and be sure to join the Comet ML Slack community<\/a> for any questions!<\/span><\/p>\n\n\n\n

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Nvidia Merlin<\/b><\/td>An open source framework for building high-performing recommender systems<\/span>

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RecList<\/b><\/td>An open source library for behavioral, \u201cblack-box\u201d testing for recommender systems<\/span>

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Fidelity Mab2Rec<\/b><\/td>An open source framework for <\/span>building contextual multi-armed bandits recommenders<\/span>

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RecSys Reproducibility Paper at TMLR\u201922<\/b><\/td>D. Kilit\u00e7io\u011flu, S. Kad\u0131o\u011flu, Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It, Transactions on Machine Learning Research (TMLR) 2022<\/span>

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https:\/\/openreview.net\/pdf?id=sX9d3gfwtE<\/span><\/a><\/p><\/td><\/tr>

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