Create a progression of recommendations from a user-supplied recommender. For more info, see our official docs
This package was made for a specific use-case but should work with any content-based recommendation engine and possibly user-based recommenders.
The initial use case was to create a visual progression of artwork recommendations. This was done to create interesting collections of fine artworks powered by our computer vision recommendation engine.
To see an example, see our samples.
pip install graph-progression
Progession has a full sample set to show the functionality of the package w/o the need of a recommender.
from graph_progression import DFSProgressor, random_walk
progressor = DFSProgressor()
progressor.create_progression(0, random_walk, progression_length=3)
# [0, 8114, 9353]
progressor.graph
# {
# 0: [8114, 5298, 2050, 4837, 1376, 2924, 8689, 8000, 2171, 3246],
# 8114: [9353, 8647, 8768, 8391, 8555, 8879, 9102, 9481, 9282, 8142]
# }As you can see, progession generates "recommendations" at each step, uses them to populate a graph (for memoization), and then continues on. Not shown here, but it also has the intelligence to backtrack using the graph if it ends a recommendation (node) with no recommedations (edges).
- Recommendation progression via sequential depth-first search
- Memoization
- Flexible API that can take as inputs:
- Starter graph
- Recommendation algorithm
- Post recommendation filters
- Selection algorithm
- Progession length
This package was created with Cookiecutter and the mgancita/cookiecutter-pypackage project template.
On commit to main, the pypi_publish.md action is run by GitHub which builds and publishes this package to PyPI.