Nearest Neighbours Recommendations #14
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still todo:
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This adds a fast and memory efficient of Item-Item KNN Recommendation models. Calculating the Similarity matrix is based on the algorithm described in the paper 'Sparse Matrix Multiplication Package (SMMP)' (www.i2m.univ-amu.fr/~bradji/multp_sparse.pdf), but modified so that only the top K rows are selected using a heap. This means that we can calculate the similarity matrix even when the full similarity matrix wouldn't fit in available memory. This calculation is also parallelized unlike the sparse matrix multiply in scipy. Also switch to using C++ instead of C for Cython, run flake8 on the Cython code, add an isort check and cpplint check, and fix some issues with the ALS unittest intermittently failing.
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Thanks for providing this very neat package. |
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Performance should be identical between C++ and C. The API's and functions are also compatible from Python - I changed to C++ mainly to use the heap functions provided with the STL: https://github.com/benfred/implicit/blob/master/implicit/nearest_neighbours.h#L21 |
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This adds a fast and memory efficient of Item-Item KNN Recommendation models.
Calculating the Similarity matrix is based on the algorithm described in the
paper 'Sparse Matrix Multiplication Package (SMMP)'
(www.i2m.univ-amu.fr/~bradji/multp_sparse.pdf), but modified so that only the
top K rows are selected using a heap. This means that we can calculate
the similarity matrix even when the full similarity matrix wouldn't fit in
available memory. This calculation is also parallelized unlike the sparse matrix
multiply in scipy.
Also switch to using C++ instead of C for Cython, run flake8 on the Cython code,
add an isort check and cpplint check, and fix some issues with the ALS unittest
intermittently failing.