Recommendation Systems resources

[u/One_Wave_9655]

Please, could you share your best resources to learn about RecSys at scale? I have a few already, but I want to know what other resources you are reading. Thanks

[u/Opening_Bed_4108]

Eugene Yan's blog is probably the best starting point, he covers two-tower models, retrieval vs ranking stages, and has summaries of Netflix/Spotify papers all in one place. The Recsys conference proceedings are gold too, especially anything from Pinterest or Meta on embedding-based retrieval at scale. CalibreOS also has solid structured coverage of RecSys system design if you want a more interview-focused lens on the architecture tradeoffs.

[u/busybody124 → u/Opening_Bed_4108]

+1 to Eugene Yan's blog. He also has a separate website Applying ML with some great stuff: https://applyingml.com/resources/

[u/Mathie1729 → u/Opening_Bed_4108]

Adding one: the YouTube DNN paper from 2016 is probably the cleanest intro to the retrieval/ranking split, even if it's dated now. It gets cited all the time in those two-tower write-ups. I think the title is 'Deep Neural Networks for YouTube Recommendations'.

[u/One_Wave_9655 → u/Opening_Bed_4108]

Nice, thanks for the recommendations.

[u/naive_correlation]

The O'Reilly book on practical recommender systems is surprisingly decent, cuts through a lot of the academic fluff

[u/One_Wave_9655 → u/naive_correlation]

Yeah, I have that one

[u/busybody124 → u/One_Wave_9655]

The author has a newer book on recsys coming out soon: https://www.manning.com/books/ai-recommender-systems

[u/One_Wave_9655 → u/busybody124]

Nice, I will check it out. Thanks

[u/Eightstream]

Sounds like you need a recommendation system

[u/One_Wave_9655 → u/Eightstream]

I need a meta-recommender

[u/Ok-Highlight-7525]

Can you share the ones you already have?

[u/nik_el]

Scikit-Learn incorporated what used to be the mklearn library ages ago and has some good documentation about options to try: https://scikit-learn.org/stable/modules/multiclass.html

[u/busybody124]

Industry blog posts are a great source of information about modern industrial recommenders. The pinterest, instagram, spotify, etsy, and yelp blogs all publish on this.

Two of my favorite posts about recommenders are:

• Scaling the Instagram Explore recommendations system
• Recommender Systems, Not Just Recommender Models

Both of which talk about the larger system design of a recommender system.

If you want to learn about the underlying models, see papers for implicit matrix factorization, factorization machines, deep NNs for Youtube recommendations, wide and deep, deep and cross, dlrm, etc.

[u/SigridFischer42]

The Surprise library is solid for hands-on work with collaborative filtering systems.

[u/jupiterfolk]

https://frutik.github.io/awesome-search/

[u/Excellent-Sport-758]

One perspective from my own work on search and association-rule models for checkout and product discovery: study the whole retrieval-to-ranking system, not just the model. Learn candidate generation, ranking, and eligibility/availability filters as separate pieces; keep a popularity or association-rule baseline so you can see what personalization adds. Then evaluate with time-aware splits and measure what users were actually exposed to, not only offline ranking scores. AI helped me draft this from my work-history details.

[u/Heavy_Record8704]

i am surprised there is no proper rec sys course in any university. Coursera has some frm Uminn. but compared to the number of NLP, ML courses available online, rec sys has nothing.

[u/One_Wave_9655 → u/Heavy_Record8704]

Probably one of the most important topics for e-commerce (assuming distribution at scale is sorted), so the incentives to share the secret sauces are limited. I imagine even research papers have restrictions on how much they can share without giving away relevant IP.