recommenders VS NumPy

recommenders – Best Practices on Recommendation Systems

I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.

Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]