Recommender System: Resources

thank you to unDraw for the above illustration

This is where I post my resources for recommender system. It may be messy at this moment. ongoing updates.

Courses:

Google: ML Recommenders Course for beginners (FREE):

https://developers.google.com/machine-learning/recommendation/

Udemy: ML Recommenders with Python (Personal Plan):

https://www.udemy.com/course/building-recommender-systems-with-machine-learning-and-ai/

Articles:

Matrix Factorization:

understanding matrix factorization for recommendation

Embeddings:

embeddings crash course by Google

ML Glossary (RecSys):

Recommenders glossary by Google

Architecture:

https://amatriain.net/blog/RecsysArchitectures

https://eugeneyan.com/writing/system-design-for-discovery/

https://medium.com/nvidia-merlin/recommender-systems-not-just-recommender-models-485c161c755e

https://fennel.ai/blog/real-world-recommendation-system/

Resources:

https://aman.ai/recsys/index.html

https://amatriain.net/blog/recsys-resources/

https://aman.ai/recsys/papers/

https://github.com/jihoo-kim/awesome-RecSys

https://medium.com/@joankusuma/list/recommender-system-24441884fc22

System Design:

https://medium.com/double-pointer/system-design-interview-recommendation-system-design-as-used-by-youtube-netflix-etc-c457aaec3ab

Follow-along Projects for beginners:

TensorFlow Recommenders (TRFS):

start with quickstart, and then retrieval, and then ranking

https://www.tensorflow.org/recommenders/examples/quickstart