If you want to run some analysis on a dataset that's just a little too big to load into memory on your laptop, but you don't want to leave the comfort of using Pandas dataframes in a Jupyter notebook, then Dask may be just your thing. Dask is ...
The ALS-WR algorithm works well for recommender systems involving a sparse matrix of users by items to review, which happens when most people only review a small subset of many possible items (businesses, movies, etc.). By tweaking the code from a great tutorial to take advantage of this sparsity, I was able to dramatically reduce the computation time.
In my PhD research, I do a lot of analysis of 2D and 3D grid data output by simulations I run. In my analyses, it's very helpful to restructure these data into a more useable format. A few key lines of python code do the trick.
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