metarank A low code Machine Learning peersonalized ranking service for articles, listings, search results, recommendations that boosts user engagement https://github.com/metarank/metarank
Learn-to-Rank with OpenSearch and Metarank https://blog.metarank.ai/learn-to-rank-with-opensearch-and-metarank-3557fa70f8e8
https://github.com/NicolasHug/Surprise Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
Provide various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others. Also, various similarity measures (cosine, MSD, pearson...) are built-in.
TensorFlow Recommenders is a library for building recommender system models using TensorFlow. https://github.com/tensorflow/recommenders
recmetrics https://github.com/statisticianinstilettos/recmetrics A library of metrics for evaluating recommender systems
MLOps Meetups: Testing Recommender Systems with Jacopo Tagliabue https://www.youtube.com/watch?v=_EG6HodKcXU
ML System Design Jam: Recsys Session 1 (Google’s intro to RecSys ) by Chloe He
https://www.youtube.com/watch?v=BdY6rzYwFe0
ML System Design Jam: Recsys Session 2 - Wide and Deep Learning by Jin Yun Soo and Han-chung Lee
https://www.youtube.com/watch?v=PRjfT_Lt0uk
Machine learning is going real-time
https://huyenchip.com/2020/12/27/real-time-machine-learning.html
Introduction to streaming for data scientists
https://huyenchip.com/2022/08/03/stream-processing-for-data-scientists.html#from-table-to-log
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
https://arxiv.org/pdf/2005.12002.pdf https://huyenchip.com/machine-learning-systems-design/exercises.html#exercises-rWl8SQW https://medium.com/hackernoon/how-we-grew-from-0-to-4-million-women-on-our-fashion-app-with-a-vertical-machine-learning-approach-f8b7fc0a89d7
Design a machine learning system
https://huyenchip.com/machine-learning-systems-design/design-a-machine-learning-system.html#scaling-49BpIQl https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
What I learned from looking at 200 machine learning tools https://huyenchip.com/2020/06/22/mlops.html?